5G White Paper: Data Driven and Intent Aware Smart ...

5G White Paper: Data Driven and Intent Aware Smart Wireless Network

Data Driven and Intent Aware

Smart Wireless Network

2019


Executive Summary

To accommodate a wide range of scenarios and service requirements, the 5G network is becoming

more agile, and inevitably, more complicated, thus posing great challenges to network management

and radio resource optimization. Intelligence is expected to be introduced in wireless network for all

the domains and all levels, from local, edge to the cloud. Data analytics, machine learning and

artificial intelligence are identified as the key drivers for the intelligence evolution and revolution in

the wireless network. Data driven smart wireless network usage scenarios and network architecture

are being heatedly discussed and investigated recently both in the wireless network industry and

academia. “Smart and Simplicity” become the industry consensus for 5G and future network.

“Smart” requires the network to dynamically adapt to the diversified scenarios and services to

efficiently boost both spectrum efficiency, energy efficiency and offer the consistency the user

experience. “Simplicity” poses the requirements of network automation to significantly reduce the

network operation and maintenance human labor and cost.

This white paper investigates the data driven and intent aware smart wireless network. It is an update

and evolution of the previous whitepapers “Wireless Big Data for Smart 5G” and “Wireless Big Data and

AI for Smart 5G & Beyond ”, where the reference architecture and use case are comprehensively studied.

In this white paper, data driven wireless network is firstly introduced and reviewed. Different

categories of typical use cases and potential solution are described, from the network management

and operation to MEC optimization, the network resource optimization and the intelligent

transmission technologies. The ML and data analytics algorithms utilized in the all the use cases of

the series of whitepapers are also discussed and summarized to provide some insight for the

researcher on the future works. Furthermore, intent aware wireless network is proposed and

investigated to achieve the network “simplicity” leveraging the data driven network intelligence. The

concept and motivation of the intent aware network is firstly briefly described. The reference

architecture of intent driven is investigated with discussion of the intent expression. Besides, the key

issues, challenges and the intent service evolution roadmap are presented. Last but not the least, the

standardization progress are discussed.

We wish this white paper can provide readers with some valuable insights for industry on the data

driven and intent aware smart wireless network and help to accelerate the process of smart wireless

network.


1 中文摘要

为了适应多种多样的场景和服务需求，5G网络正变得越来越灵活，也不可避免地变得越来越复杂，给

网络管理和无线资源优化带来了巨大挑战。为了应对挑战，智能有望被引入到从本地，边缘到云的无线网

络的所有域和所有层级。“智能极简”成为5G和未来网络的行业共识。“智能”要求网络动态适应不同的

场景和服务，以有效地提高频谱效率，能效并提供定制化的极致用户体验。“极简”提出了网络自动化的

需求，以显着减少网络运维的人力和成本。

本白皮书研究了数据驱动和意图感知的智能无线网络。这是对先前“无线大数据和智慧5G”和“无线

大数据与人工智能使能的智慧5G+”系列白皮书的更新和发展，先前两本白皮书中对数据驱动的智能网络参

考架构和用例均进行了研究和分享。在本白皮书中，首先回顾了数据驱动的无线网络，更新了网络运维、

边缘计算优化、网络资源优化和智能传输技术的典型用例及潜在解决方案的进展；其次还进一步分析了智

能无线网络用例所使用的机器学习、数据分析算法及数据需求，希望能为研究人员对未来的工作提供一些

参考。

本白皮书中还进一步提出了意图感知（或者意图驱动）的无线网络，研究如何简化无线网络管理接口，

利用数据驱动的智能化手段将网络管理意图自动转换为复杂的网络管控操作，以更好的实现网络自治化，

减少运维人员的人力和工作复杂度，并进一步面向垂直行业提供更简易的网络管理能力开放。文中首先简

要描述了意图感知网络的动机和概念，并通过讨论意图感知的无线网络场景和用例，进一步研究了意图感

知无线网络的参考架构。其次提出了意图感知无线网络的关键问题和挑战，并对意图感知无线网络的演进

路线进行了讨论。最后，本白皮书总结了ITU-T/3GPP/ETSI等在数据采集、数据分析、机器学习、意图驱动

与智能5G网络相关的标准化进展。

我们希望本白皮书可以和读者分享有关数据驱动和意图感知的智能无线网络的一些有价值的技术探索

以及学术、产业界的最新进展，为学术界及产业界在研究、规划和设计5G 网络智能化相关技术、产品和解

决方案时提供一些参考和指引，从而加快智能无线网络的技术发展与产业化落地。


Table of Contents

2 Smart Wireless Network ...................................................................................................................................... 1

2.1 Data Driven Wireless Network ................................................................................................................ 1

2.2 Intent Aware Wireless Network .............................................................................................................. 2

3 Data Driven Wireless Network ............................................................................................................................ 4

3.1 Overall Introduction ................................................................................................................................. 4

3.2 Network Management Optimization........................................................................................................ 4

3.2.1 Use case 1: Anomaly Detection with KPIs .................................................................................. 4

3.2.2 Use case 2: Anomaly Diagnosis with KPIs ................................................................................. 7

3.2.3 Use case 3: Smart Carrier Licence Resource Scheduling ............................................................ 9

3.2.4 Use case 4: Geo Data Enhanced Coverage Estimation and Network Planning ......................... 13

3.3 MEC Optimization ................................................................................................................................. 14

3.3.1 Use case 5: Intelligent MEC Caching Optimization .................................................................. 14

3.4 Network Resource Optimization ........................................................................................................... 16

3.4.1 Use case 6: AI Empowered QoE Optimization ......................................................................... 16

3.4.2 Use case 7: Smart Resource Management for Network Slicing ................................................ 17

3.4.3 Use case 8: Data Driven Predictive Resource Allocation .......................................................... 22

3.4.4 Use case 9: Big Data Analysis for Handover Optimization in Massive-MIMO Cellular Systems

30

3.5 Radio Transmission Technologies ......................................................................................................... 35

3.5.1 Use case 10:Deep learning aided codebook design for grant-free NOMA ................................ 35

3.6 Discussion on the wireless big data and AI/ML algorithms for wireless network applications ............ 38

4 Intent Aware Wireless Network ........................................................................................................................ 40

4.1 Motivation ............................................................................................................................................. 40

4.2 Use Cases for intent aware wireless network ........................................................................................ 42

4.2.1 Use Case 1: Intent driven network service provision ................................................................ 42

4.2.2 Use Case 2: Intent driven network provision and assurance for 5G vertical services ............... 42

4.2.3 Use Case 3: intent/policy driven network optimization for user QoE assurance ....................... 43

4.2.4 Use Case 4: Intent driven network performance optimization ................................................... 44


4.2.5 Use Case 5: intent driven traffic steering optimization .............................................................. 45

4.3 Reference Architecture for the intent aware wireless network .............................................................. 46

4.4 Intent Expression Model ........................................................................................................................ 47

4.5 Technical Key Issues ............................................................................................................................. 48

4.6 Intent Service Evolution Consideration ................................................................................................. 49

5 Standards Progress in 3GPP/ITU/ETSI and Consideration ............................................................................... 49

5.1 ITU-T ML5G ......................................................................................................................................... 49

5.2 3GPP ...................................................................................................................................................... 50

5.2.1 SA2 ............................................................................................................................................ 50

5.2.2 SA5 ............................................................................................................................................ 50

5.2.3 RAN3 ......................................................................................................................................... 52

5.3 ETSI ENI ............................................................................................................................................... 52

6 Summary ............................................................................................................................................................ 53

7 Reference ........................................................................................................................................................... 53

Acknowledgement ..................................................................................................................................................... 54

Abbreviation .............................................................................................................................................................. 55


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2 Smart Wireless Network

2.1 Data Driven Wireless Network

5G is on the road of commercialization all around the world. During the standardization of 5G, the network is

defined to satisfy all the requirement of eMBB, URLLC and mMTC, so it is also called internet of everything. This

means the network will become more sophisticated than ever, more scenario and larger number of functionalities

will be used in the network. Based on this requirements and premise, it will be a very tough to use traditional

method to manage and configure the network. So some new technologies are introduced to the wireless

communication of 5G network. Big data analytics and machine learning are envisioned to enable the automation

operation and maintenance of the future wireless network and also help to ensure customized user experience.

Figure 1.1 shows the general close loop flow of data analytics. Which mainly consists the following steps: 1) data

collection and context aware; 2) Diagnostic analysis;3) predictive analysis; 4) Reasoning and decision making.

Figure 1.1 Data analytics closed loop control

“Data Driven” is widely recognized as the key features of 5G and future network by the mobile network industry.

Data analytics and machine learning are expected to empowered the network with the following capabilities;

Reliable prediction: The vase multi-dimension data collected from the network enables the capability to

predict the network traffic, network anomaly, service pattern/type, user trajectory/position, service quality of

experience, radio finger print, interference, etc. These predictions will undoubtedly empower the proactive network

management and control, leading to significantly improved network resource and energy efficiency and customized

user experience assurance.

Advanced network optimization and decision: Driven by the collected data from the real network, data


2

analytics and machine learning can help to efficiently solve massive of problems in the 5G network which are

usually hard to model or suffering great computation complexity due to the extremely high dimensions or

NP-hardness.

The concept, use cases and architecture framework of data driven wireless network are extensively discussed in the

previous versions of the whitepaper [1][2].Data analytics and machine learning can be implemented and utilized in

different management and control loops of the wireless network. In the reference architecture of [1-3], various

levels of data analytics functionalities were introduced in the wireless network, including the management plane,

core network and RAN. Based on which, diversified use cases are supported [1,2,4].More and more research

academics, industry companies and SDOs are joining the work on data driven smart wireless network. Great

progress had been made in both standardization and test/trials/application in the commercial network or 5G test

network. The main updates and progress are described in detail in the section 2 and section 4.

2.2 Intent Aware Wireless Network

Compared with previous data driven network, the intent aware network mainly adjusts and optimizes according to

the intention of human and machine instead of data. Data driven technology is one of the important fundamental

technologies to automatically realize the management intent. With the requirements described in intent, the

data-driven wireless network may automatically uses the knowledge learned from historical data to figure out the

actions based on currently network state.

In intent aware wireless network, the network operation staff uses controlled language or DSL to express the

management goals, without describing how to accomplish the goals. The network is responsible to adjust and

optimize itself to satisfy the input intent. Specifically, the network needs to:

⚫ Enable new digital business. It needs to have the flexibility to quickly align with rapidly changing business

objectives.

⚫ Be easier to configure, operate and maintain in large number of differential usage scenario with growing scale

and complexity. Current operational modes are not scalable or sustainable to fit the requirement.

⚫ Provide full visibility in terms of how a network is operating and providing assurance to support the desired

business and be able to identify any discrepancies and recommend correction.

⚫ Identify and neutralize security threats before they cause harm. Multi-cloud, IoT and mobile adoption open up


3

new threats that the network needs to be constantly protect against.

Intent aware network offers a significant paradigm shift in how networks are planned, designed, operated and

optimized. In the past, the management intents are translate to device-level configurations manually, and the

network management staff is responsible to assure the network to reach the desired status. The network designer or

operator had to manually derive individual network-element configurations to support the desired intent such as, “I

want these servers to be reachable from these branches”. Then, specific configurations (e.g., VLAN, security rules

etc.) are derived and configured on each device manually. The intent aware network replaces conventional

management and optimization activities that requires manually derived network-element configurations with the

intent expression that just describes “what to do” rather than “how to do”. The network will itself automatically

figure out “how to do”.

Figure 1.2 Conceptual description of Intent aware network

Figure 1.2 shows the conceptual description of intent aware wireless network.

⚫ The translation function is about the characterization of intent. It enables network operators to express intent in

a declarative format, expressing what the expected networking behavior is that will best support the business

objectives, rather than how the network elements should be configured to achieve that outcome.

⚫ The action function captured intent then needs to be interpreted into policies that can be applied across the

network. The action function installs these policies into the physical and virtual network infrastructure using

Human(thoughs)

Machine

Translation

Activation

Network(wired, wireless)

Thoughts and Intent

Capture business intent, translate to policies, check integrity

Orchestrate policies, configure system

Feedback network parameter and status

Assurance

Continuous verification, correct actions


4

network wide automation.

⚫ The assurance function continuously monitors and checks that the expressed intent is enforced by the network

at any point in time, which maintains a continuous validation-and-verification loop. The network status and

large scale configuration parameter is sent back to translation module to correct the status of the network to

satisfy the intent.

3 Data Driven Wireless Network

3.1 Overall Introduction

To foster the exchange of ideas, sharing of research on data driven smart wireless network, three white papers are

published in 2017 and 2018.[1-3]. The white paper introduced the use cases, potential solutions, reference

architecture, procedure and platform designs on the wireless big data and AI driven network. Based on the previous

versions of the white paper, this white paper mainly focus on the expansion of data driven use cases and also

introduces the intent aware smart wireless network.

In this section, ten use cases are described to update the progress and further demonstrate promising research

topics of data driven smart wireless network from four aspects: network management optimization, MEC

optimization, network resource optimization and radio transmission technologies. The AI algorithms

3.2 Network Management Optimization

3.2.1 Use case 1: Anomaly Detection with KPIs

KPIs anomaly detection is quiet important for network maintenance. Due to the complexity of 5G network which

contains numerous radio nodes and other components, there are a huge amount of KPI data to be monitored, which

can be time consuming, error-prone and even impossible. A ML-based anomaly detection method is considered, as

shown in Figure 2.1. It is essentially composed of three modules: anomaly detection, anomaly scoring and feedback

module. The anomaly detection model and scoring model are trained with off-line data, using the batch computing

engine and training engine in Figure 2.2. Then, the KPIs data are detected online based on the streaming computing

engine. The KPI data point whose anomaly score is higher than a predefined threshold will be noticed to the O&M

engineer and the engineer can label it whether abnormal, providing feedback to the training module to improve the

algorithm performance.


5

Figure 2.1 An illustration of ML-based anomaly detection method

The KPIs represent varied characteristics because of the diverse characteristics of network modules. For example,

some KPIs show periodicity while others do not; some KPIs have trend, while the other KPIs are stable. A two

stage modelling method is investigated to deal with the huge challenge for comprehensive modelling all kinds of

KPIs. As shown in Figure 2.2, the first stage is the classification stage, where a time series clustering algorithm is

formulated to classify the KPIs based on their structure characteristics. In the second stage, the module selects an

appropriate time series model for each category of KPI, predicting the normal baseline at each time point for a KPI.

In the online detection, a value would be denoted as anomaly if it exceeds the baseline.

Figure 2.2. A demonstration of two stage time series modelling method

The structural features based time series clustering method can be used to reduce the complexity of clustering.

Firstly, the time series are classified into two main categories, with significant periodicity and non-significant

periodicity, based on Fourier transformation. Secondly, the K-means algorithm is used to cluster the time series in

each main category base on seven features extracted from the KPI series. In the first stage, the frequency amplitude

spectrum of a KPI is calculated by discrete Fourier transformation (DFT) as follows:

21

0

[ ] ( ) , 0 1N j kn

N

n

F k x n e k N− −

=

= −.

Denoting the maximum, mean and standard deviation of the amplitude spectrum as maxF

, meanF

, stdF

.

If satisfying the following condition:

max mean stdF F c F + ,

where c is a predefined coefficient larger than 3, the KPI would be classified as significant periodicity,

otherwise non-significant periodicity.


6

When a KPI has been classified, a suitable time series model is selected according to its characteristic. There

are a number of candidate models available, such as density estimation, Olympic model, regression model,

Holt-Winters model, auto-regressive integrated moving average (ARIMA) [5]. Fox example, if a KPI contains

trend and periodicity, the Holt-Winters model is able to model it as following:

* *

1 1

* *

1 1

* *

1 1

( ) (1 )( )

( ) (1 )

( ) (1 )

t t t m t t

t t t t

t t t t t m

l x s l b

b l l b

s x l b s

− − −

− −

− − −

= − + − +

= − + −

= − − + − ,

where tl , tb , ts are the level component, trend component and seasonal component respectively, and m is

the period of time series. The forecasting value at h step would be:

|m

t h t t t t m hx l hb s ++

− += + + ,

where 1mod)1( +−=+ mhhm

. When the prediction value and fitting errors in historical data are calculated,

the normal baseline could be formulated as:

| 1 /2

| 1 /2

t h

t h

L

t h t H

U

t h t H

x x z

x x z

+

+

+ −

+ −

= −

= + ,

where 𝑧1−α/2

is the 1 − α/2

percentile of standard Gaussian distribution, and H is the standard deviation

of fitting errors in historical data. A common used value for α is 0.003. Figure 2.3 is an illustration of the

computed thresholds for a KPI.

Figure 2.3. An illustration of time series modelling by Holt-Winters. The upper figure represents the true value (blue curve) and

fitting value (green curve) in historical data; the lower figure represents the true value (blue curve) and the predicted thresholds (red

curve) in the following day.

The other types of KPI can be modeled by other time series models. For example, the data with significant


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randomness could be modeled by density estimation, rather than Holt-Winter model. The anomaly scoring model is

critical to reduce the false alarm and facilitate the O&M engineer to focus on important events. The detailed

algorithm is a planning research topic in the future.

3.2.2 Use case 2: Anomaly Diagnosis with KPIs

When a KPI anomaly is detected , it is quite worthy to define the root causes for rapid fault recover. Figure. 2.5

depicts the anomaly diagnosis method developed in this paper, which combines a rule-based diagnosis module and

a ML-based diagnosis module to handle wide range of scenarios.

As shown in Figure 2.5, when the detected anomaly is a known fault which can be explicit diagnosed by

predefined expert rules, the rule-based diagnosis module could define the root causes according to several useful

information, such as the network experience (NE) model in Figure 2.5 which contains the network topology, the

exact mathematical function between the KPI and relating counter indicators (counter indicators are more basic

performance data, comparing to KPIs), and expert rule library. The rule-based module generally can output exact

rule causes, and provide direct execution suggestion for recovering.

Figure 2.5. A mixed scheme which combined rule-based diagnosis module and ML-based diagnosis module for KPI anomaly

diagnosis

When the detected anomaly is an unknown fault, the ML-based diagnosis module would define the root causes

by using the partial least squares regression (PLS) algorithm. The PLS has been used in multivariate monitoring of

process operating performance, which is almost exactly in the same way as PCA-based monitoring [6]. Instead of

only finding hyper-planes of maximum variance for independent variables, PLS finds a linear regression model by

projecting the response variables and the independent variables to a new space. Compared to standard linear

regression, PLS regression is particularly suitable when the dimension of response variables is more than

independent variables, and when there is multi-collinearity among independent variables. As illustrated in Figure


8

2.6, when a KPI is detected abnormal, PLS models the KPI as a response variable and the correlate counter

indicators as independent variables. Following the PLS modelling, the contribution analysis was conducted to find

the top root counter indicators.

Figure 2.6. Root cause analysis with PLS model when a KPI is abnormal.

Denoting the data matrix of correlate counter indicators as X and the matrix of a KPI as Y , the PLS model

between X and Y can be formulated as:

FUQY

ETPX

T

T

+=

+= ,

where T and U are projections of X (the X score, component or factor matrix) and projections of Y (the Y

scores), respectively; P and Q are orthogonal loading matrices; and matrices E and F are the error terms. As the

PLS model has only one response KPI, the PLS1 algorithm can be used for estimating the T, U, P and Q. And then,

a 2T statistics is used to represent the model status at each observation x as:

22x=T ,

where 1/21 )(R TR−= , TT

n

T

1

1

−= and R is the rotation matrix for X. The contribution of i-th

independent variable, i.e. counter indicator, to the 2T statistics is calculated as:

22 ),( xiiTC = ,

where i is the i-th row of . The total contribution of i-th counter indicator to the variation of the KPI can

be calculated as the sum of ),( 2 iTC from n observations. The contributions of all counter indicators are sorted,

and the top-n counter indicators are output as the root causes for the anomaly KPI. Figure 2.7 shows an

experimental example, illustrating the contributions of 60 counter indicators to a anomaly KPI, DL IP Throughput.

The O&M expert confirms that the top counter indicator, DL Used CCE Average Number, is useful for the

anomaly diagnosis, demonstrating the effectiveness of the proposed algorithm.


9

Figure 2.7. An experimental example of PLS method for root cause analysis

3.2.3 Use case 3: Smart Carrier Licence Resource Scheduling

Operators are facing challenge of increasing of user data traffic. However, increasing network capacity through

infrastructure investment is not an economical way. Considerng the tiding effect of the network traffic, carrier

license scheduling is considered as a critical solution in situations where infrastructure investment is limited.

However, the existing method strongly relies on the experience of network maintenance staff, and an automatic and

efficient method is needed to help implement carrier license scheduling by predicting network traffic. AI/ML

algorithm can been used for the network traffic prediction. In this section, a Smart Carrier License Resource

Scheduling (SCLRS) is introduced, which is based on the AI/ML considering the spatio-temporal distribution

characteristics of network traffic.

a) Overall architecture of SCLRS

Based on the network optimization platform in the current network, we design SCLRS architecture as shown in

Figure 2.8. In Figure 2.8, the data collector will collect data from RAN. The data customization center in the

network optimization platform can customize the required data (KPI, KQI, and so on) for different applications. For


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the SCLRS, data customization center will receive KPI data related to carrier licence scheduling, as the data list has

been sent from data customization center to the data collection service. Data customization center send KPI data to

carrier licence scheduling strategy generation center, which will predict the trend of carrier expansion. Then,

according to the prediction result, the strategy execution center designs a strategy for carrier licence scheduling,

and the command module will send a carrier license scheduling command based on this strategy. Finally, OMC will

enforce the carrier license scheduling towards the RAN.

OMC

Network optimization platform

collect data

Send carrier license scheduling

command

Monitoring and visualization unit

Data collector

eNB

eNBeNB

RAN

Data

customization

centerCommand Module

Strategy

execution

center

Carrier licsence

scheduling

strategy

generation center

Figure 2.8 overall architecture

2) Implementation of prediction algorithm based on AI

The carrier expansion prediction based on AI/ML can been realized in carrier license scheduling strategy

generation center, which includes the following parts shown in Figure 2.9: data extraction unit, prediction model

training and inference unit, and carrier license scheduling policy generation unit. Data extraction unit further

extracts KPI data according to the required data format, such as time collection interval, granularity, index list, and

so on. Then, in the prediction model training and inference unit, the extracted data is further processed, such as data

reordering, data cleaning, and data structuring. Following the data processing is the model training (model update)

and model inference. By the trained prediction model, we can predict the future value of the KPIs. And then, carrier

license scheduling policy generation unit formulates scheduling strategies based on the predicted results and the

existing algorithms of the network optimization platform.


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Data processing

Model training

Model inference

Data extraction unit

Data cleaningData

structuring

Prediction

model training

Data

reordering

Data for model training

Data for model inference

Carrier license scheduling policy generation unit

Prediction model training and inference unit

Prediction

result

Carrier license scheduling strategy generation center

Prediction

model

Real-time data

Historical data

Model update

Figure 2.9 Carrier license scheduling policy generation unit

3) Prediction algorithm based on spatio-temporal characteristics

Training a unique model for each base station in the network is computationally and time consuming. Therefore, it

is a practical method to train a unified prediction model for all base stations in a selected area. In this section, we

will introduce an improved densely connected convolutional neural networks (CNN).

(a) Spatio-temporal correlations

There is strong space-time correlation between base stations. Figure 2.10 shows the weekly empirical mean of

mobile traffic of one typical base station, and the actual sampled traffic as well as the standard deviation are also

showed in the same figure. According to Figure 2.10., we can observe that although some samples deviate far from

the mean values, the periodicity of the mean curve is quite obvious, which illustrates that the mobile traffic in

certain area has strong autocorrelation.


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Figure 2.10. Traffic volume sampled over 13 weeks, weekly empirical mean, andstandard deviation in one base station.

Table. 2.1 shows the Pearson correlation coefficients of five representative base stations. It can be seen that good

spatial cross-correlation exists between the BSs.

Table 2.1 Pearson correlation coefficients among representative base stations.

BS. 1 BS. 2 BS. 3 BS. 4 BS. 5

BS. 1 1.0000 0.3387 0.2963 0.3618 0.3524

BS. 2 0.3254 1.0000 0.3579 0.6303 0.6272

BS. 3 0.2263 0.3247 1.0000 0.4060 0.4273

BS. 4 0.4222 0.4783 0.3966 1.0000 0.6952

BS. 5 0.4065 0.5329 0.4200 0.6495 1.0000

In summary,there exists important spatio-temporal correlations among traffic patterns of base stations.

Consequently, both spatial and temporal features can be utilized to improve the precision of prediction when

designing prediction models.

(2) Grid image modeling method

In consideration of the difficulties of determining the specific coverage of base stations, we use smaller grids to

estimate base stations coverage. As for the base stations distributed in areas, we use longitude and latitude to divide

the selected area into a two-dimensional grid image. Figure 2.11 illustrates the griding process and how to map the

traffic data to the neural network tensors.


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(a) Base station distribution in selected area. (b) Distributed area griding. (c) Spatio-temporal multiple input data.

Figure 2.11 Geographical region griding process.

The input data is grouped into closeness dependence data and period dependence data. Different groups of the input

data can be defined as:

• Closeness dependence data: the flow grid images of the previous p hours.

• Period dependence data: the flow grid images of the corresponding time of the past q days.

(3) Convolutional prediction model

A densely connected CNN is used to capture the spatial-temporal features of the traffic data among different

grids in selected area. One example of the DenseNet structure is made up of a 6-layer dense block with the growth

rate of k = 12, which can be seen in Figure. 2.12.

Figure 2.12 The DenseNet Structure.

In order to extract the features of closeness dependence data and period dependence data, two parallel DenseNet models

are used. The inputs for this two DenseNet models are flow grid images of closeness dependence data and period

dependence data, and the output of the whole model is 𝑋𝑜 = 𝜎(𝑋𝑐 + 𝑋𝑝), where Xc and Xp is the output of two dense

blocks model, and σ denotes the sigmoid function.

3.2.4 Use case 4: Geo Data Enhanced Coverage Estimation and Network Planning

With wireless network positioning technology evolution, e.g. Global Navigation Satellite System (GNSS),

Finger-printing, TDOA and so on, coverage measurement (RSRP/RSRQ) with location information of UE can be

collected by using MDT. Base on this, the management and monitor granularity of coverage performance changes

from cell level to scenario area or even location bin level. However there are still some problem in the coverage

estimation process. From the network management view, different scenario or area should have differentiated

coverage requirement. In general, urban area should take tougher criterion than rural area. But actually, some rural


14

areas with high population density and developed economy do exist. Operators need distinguish scenarios’ trait

more precisely to achieve the target of refined management and planning. And from the trouble shooting view,

coverage hole will be false detected by misleading of no man’s land or MDT data insufficient. For instance, the

scenario of scenic spot in the mountain, users’ measurement only appears at the walkway areas. The coverage

estimation should focus on the walkway rather than the whole spot area.

To achieve more precise coverage estimation, one approach is adding geography information to network

management platform (planning platform, optimization platform, performance monitor platform and etc.). And

based on the Satellite Imagery Semantic Segmentation technology, the operator can collect geography information

from online satellite image at low cost and time-efficient (high resolution image is not necessary). It can recognize

the populated area boundary precisely by analyzing the density of buildings, to assist to set the proper planning KPI

requirement of different areas. Moreover it will detect every location bins’ landforms (water area, mountainous

region, farmland, buildings, roads etc.), than make a Geo-fencing for further scenario-based coverage analysis.

These information combined with MDT measurement could be helpful to correct the coverage rate estimation and

refined network optimization into scenario and bin level.

3.3 MEC Optimization

3.3.1 Use case 5: Intelligent MEC Caching Optimization

(1) Description

Mobile data traffic is anticipated to increase at a 47% compound annual growth rate (CAGR) from 2016 to 2021.

This is largely due to a tremendous growth in not only the number of mobile devices, but also the user interest

towards diverse applications with different characteristics, and demand for large-scale, flexible interconnection of

users and devices. Nevertheless, supporting such a requirement turns to be a big challenge and needs for developing

new architectures. To this end, edge-caching is introduced as one of the promising technologies.

Edge-caching supplements cloud network by deploying Multi-access Edge Computing (MEC) servers with

caching functions in close proximity to the end mobile users. Typically, MEC servers are deployed at the base

stations (BSs) of a wireless network (e.g., a cellular network) for executing delay sensitive and context-aware

applications. Upon receiving demand from users, MEC server directly brings the requested content stored locally to

the users, instead of downloading the content through the backhaul links and then delivering the content to the users.

Therefore, by serving the mobile users locally, edge-caching can alleviate the load on cloud data centers and reduce


15

the network delay, and thus improve quality of experience (QoE) of the mobile users. However, due to the random

content request and limited cache memory, the performance of edge-caching network is mainly dominated by two

mechanisms, namely, user behavior prediction and caching policy, which will be investigated in the follows,

respectively.

(2) Solutions

A. User Behavior Prediction

In the context of edge-caching network design, the user behavior prediction is to make prediction about the

possible content request in future for target mobile end user(s). The existing works on user behavior prediction

problem can be studied from the following four aspects:

• The dimension(s) of considered data has impact on the prediction design. The prediction of content

popularity or user preference can be based on single-domain data, e.g., the data from Youtube. On the

other hand, the prediction can be based on the data from several related domains, namely cross-domain

prediction. As an example, the preference of an individual user is determined by not only previous

requesting history for contents, but also the user’s social ties.

• As per prior knowledge of content popularity, the prediction problem can be categories in two

approaches: model-based and model-free. Model based prediction: the content requests are generated by a

parametric. Model free prediction: there is no assumption on the content request distribution.

• In the case of optimization target, the prediction problem can be classified into: content popularity and

user preference. The content popularity corresponds to overall performance of the network, i.e.., it focuses

on how many hits from all users in the network. Comparatively speaking, the user preference is related to

performance of individual user(s), i.e., it focuses on how many hits from an individual user or a certain

small user group with similar preference.

• In the case of optimization method, a broad family of machine learning algorithms have been studied, due

to their efficiency on solving prediction problems. The learning algorithms can be further categorized into

stochastic learning optimization and deterministic learning optimization.

B. Caching Policy

Based on the user behavior prediction results, caching policy can be designed. There are three categories of

caching policies, namely, proactively caching policy, reactively caching policy, and static caching policy. The

proactively caching policy pushes content files before they are requested by the user. The reactively caching policy

transmit content files to the user when they are requested, and then the files can be cached locally so that they can


16

be read locally if being requested again. The static caching policy is obtained by solving some deterministic

optimization problem. It determines the files to be cached deterministically and do not change them until the

system state changes.

A taxonomy for edge-caching network design can be considered for the intelligent MEC optimization. This

taxonomy provides a framework for comparative study of the existing works and hints at the development of new

user behavior prediction algorithms and caching policy. Furthermore, this taxonomy helps to analyze the

edge-caching network status and requirements as well as select appropriate model and method for design, and thus

facilitate the intelligent management and operation of the edge-caching networks.

3.4 Network Resource Optimization

3.4.1 Use case 6: AI Empowered QoE Optimization

Mobile communication system uses QoS framework to support different QoS requirements of different traffic, the

related QoS parameters are provided to the base station for radio resource allocation. QoE e.g. a MOS value is

measured at application layer based on application layer info to estimate the level of user experience. However, the

QoE is unknown in the mobile communication system. This means when the base station adjusts the radio resource

allocation for each user and the application traffic, it doesn’t have a direct estimation whether the adjustment will

be positive or negative to the user experience. This may incur the complaint from user or application service

provider later or may introduce more signaling interaction to adjust the radio resource back and forth in the

communication system. So it’s important for the network to estimate or predict the user experience based on

network side information and optimize the radio resource based on the real-time QoE prediction. Multi-dimensional

data, e.g. user traffic data, network measurement report, application layer MOS value, can be acquired and used for

offline ML model training of a QoE estimation model. The model inference will be executed in a real-time manner

in the mobile communication system to estimate or predict the user experience based on network side information.

Thus the base station could adjust the radio resource allocation more efficiently and more accurately based on the

estimation or prediction of user experience.

Moreover, the available radio bandwidth prediction can also help the application layer to dynamically adapt its

codec rate or the transport layer for the TCP optimization. In this scenario, the predicted available radio bandwidth

is expected to be opened from the radio access network to facilitate the optimization.

To support the AI powered QoE optimization, the following AI/ML models are expected:


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(1) Application classification: this model is used to identify the desired applications to be optimized. For example,

with the user plane traffic data, the cloud VR application is supposed to be identified so that specific

optimization can be applied to assure the desired QoE performance.

(2) KQI/QoE prediction model: this model is to predict the KQI or QoE of the specific application after the

application is well identified. The model is expected to use both the application level and network level data,

e..g., application codec rate/bit rate, UE level radio channel information, mobility related metrics, L2

measurement report related to traffic pattern, e.g., throughput, latency, packets per-second, inter frame arrival

time, PDCP buffer status, cell level DL/UL PRB occupation rate;

(3) Available radio bandwidth prediction model: this model is to predict the available radio bandwidth. The

network channel condition data, e.g., transport block size, channel quality indicator (CQI), MCS, are required

for training and inference of the model.

3.4.2 Use case 7: Smart Resource Management for Network Slicing

(1) Description

The emerging fifth-generation (5G) mobile systems, armed with novel network architecture and emerging

technologies, are expected to offer support for a plethora of network services with diverse performance

requirements. The concept of network slicing has been recently proposed, where the physical and computational

resources of the network can be sliced to meet the diverse needs.

To provide better-performing and cost-efficient services, RAN slicing involves more challenging technical

issues for the realtime resource management on existing slices, since (a) for radio access networks, spectrum is a

scarce resource and it is essential to guarantee the spectrum efficiency (SE); (b) the service level agreements (SLAs)

with slice tenants usually impose stringent requirements on quality of experience (QoE) perceived by users; and (c)

the actual demand of each slice heavily depends on the request patterns of mobile users.

The classical dedicated resource allocation fails to simultaneously address these problems. One potential

solution is reinforcement learning (RL). RL is an important type of machine learning where an agent learns how to

perform optimal actions in an environment via observing states and obtaining rewards. Recently, inspired by the

theory of quantile regression, a quantile regression DQN (QR-DQN) and Wasserstein generative adversarial

network with gradient penalty (WGAN-GP) to replace the value of Q(s, a) by a distribution as well as the

reputation of generative adversarial network (GAN) to approximate one distribution, a new algorithm based on

distributional RL and WGAN-GP, namely GAN-powered deep distributional Q network (GAN-DDQN) is


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considered to realize dynamic and efficient spectrum allocation per slice[7].

(2) Solutions

Under the framework of hierarchical network slicing, we consider a radio access network (RAN) scenario with

multiple base stations (BSs) , where there exists a list of available slices 1, 2,⋯ ,M sharing the aggregated

bandwidth W and having fluctuating traffic demands 𝐝 = (𝑑1, 𝑑2, ⋯ 𝑑𝑀). The objective of our work is to find an

optimal bandwidth-sharing solution 𝐰 = (𝑤1, 𝑤2, ⋯𝑤𝑀) which maximizes the expectation of the whole system

utility, which is defined as the weighted sum of SE and QoE satisfaction ratio (i.e., α ∙ SE + β ∙ QoE). Notably,

the time-varying demand at one slot depends on both the demand at previous slot and the related allocated

bandwidth, since the maximum sending capacity of servers belonging to one service is tangled with the

provisioning capabilities for this service. For example, the TCP sending window size is influenced by the estimated

channel throughput. Therefore, the traffic demand varies without knowing a prior transition probability, making the

optimization difficult to yield a direct solution. However, RL promises to be applicable to tackle with this kind of

problem. Therefore, we refer to RL to find the optimal policy for network slicing and build the system model as

illustrated in Figure 2.13. In particular, we map the RAN scenario to the context of RL by taking the number of

arrived packets in each slice within a specific time window as the state, and the allocated bandwidth to each slice as

the action.


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Figure 2.13 An illustration of GAN-DDQN for resource management in network slicing.

WGAN-GP is introduced to estimate the

distribution of action values by minimizing the distance between the estimated action-value distribution and the

distribution calculated by the Bellman optimality operator. As for the updating and training procedure, the agent

first randomly selects $m$ transitions from the replay buffer as a minibatch for training GAN-DDQN. Then, the

agent executes the Bellman optimality operator on each transition of the selected minibatch and obtains a set of

samples that describe the real distribution. Finally, we use the designed loss functions to train the generator and

descriminator networks.

(3) Performance evaluations

Table 2.2 A brief summary of key settings for traffic generation per slice


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In this part, we verify the performance of GAN-DDQN in a RAN scenario where there are three types of

services (i.e., VoLTE, video, and URLLC) and three corresponding slices in one single BS.

With the mapping as shown in Table 2.3, RL algorithms can be used to optimize the system utility (i.e., the

weighted sum of system SE and slice QoE). Specifically, we perform round-robin scheduling within each slice at

the granularity of 0.5 ms. In other words, we sequentially allocate the bandwidth of each slice to the active users

within each slice every 0.5 ms. Besides, we adjust the bandwidth allocation to each slice per second. Therefore, the

agent updates its neural networks every second. We primarily consider the downlink case and adopt system SE and

QoE satisfaction ratio as the evaluation metrics. In particular, the system SE is computed as the number of bits

transmitted per second per unit bandwidth, in which the rate from the BS to mobile users is derived based on

Shannon capacity formula. Therefore, if part of the available bandwidth has been allocated to one slice but the slice

has no service activities at that slot, such part of bandwidth would have been wasted, thus degrading the system

SE. QoE satisfaction ratio is obtained by dividing the number of completely transmitted packets satisfying rate and

latency requirement by the total number of arrived packets.

Table 2.3 The mapping from resource management for network slicing to RL environment


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Figure 2.14 An illustration of the performance comparison between different slicing schemes (GAN-DDQN, DQN,

and hard slicing).

In this part, we compare the simulation results of the proposed GAN-DDQN algorithm with other DQN-based

allocation scheme and hard slicing method. The DQN-based allocation scheme is proposed in [8], which directly

applies the original DQN algorithm to network slicing scenario. Hard slicing means that each service is always

allocated with 1/3 of the whole bandwidth, since there exist 3 types of service in total. Again, round-robin is

conducted within each slice. Figure 2.14 depicts the variations of the system utility with respect to the episode

index considering three different networ of α and β,It can be observed from Figure 2.14 that the system utility

obtained by the GAN-DDQN algorithm is significantly higher than that of the DQN algorithm and hard slicing in

the cases of α = 0.1, β = [1; 1; 1] and α = 0.01, β = [1; 1; 1] . This is due to the fact that the proposed

GAN-DDQN algorithm is built on the distributional RL, which enhances its ability to learn an optimal allocation

policy from the dynamic and unstable networking environment.


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Figure 2.15 An illustration of the SE and QoE satisfaction ratio in the different cases of α = 0.1, β = [1; 1; 1]

(shown in the top figures), α = 0.01, β = [1; 1; 1] (shown in the middle figures) and α = 0.1, β = [1; 1; 10]

(shown in the bottom figures).

Figure 2.15 presents the variations of the SE and QoE satisfaction ratio with respect to the episode index in the

different cases. It can be observed from Figure 2.15 that the QoE satisfaction ratios for both VoLTE and video

services achieve 100% along with the iterative training. However, GAN-DDQN and DQN algorithms perform

differently on the system SE and the QoE of URLLC service.

In a word, neither GAN-DDQN nor DQN can guarantee that both SE and QoE achieve a high degree at the same

time, which is due to the reason that it is still a very challenging research topic to design RL with multiple

conflicting rewarding metrics. The simulation results have demonstrated that we cannot guarantee to

simultaneously obtain superior performance for both SE and QoE. This inspiring and interesting topic still needs

future investigation.

3.4.3 Use case 8: Data Driven Predictive Resource Allocation

(1) Description

Predictive resource allocation (PRA) is a promising way to harness wireless big data by predicting user

behavior related information such as user mobility and traffic load with machine learning. It can improve network


23

throughput, reduce energy consumption, and enhance user quality of service (QoS) remarkably for non-real-time

(NRT) services (such as file download and video transmission), which has been shown able to provide large gain

over non-predictive counterparts.

To achieve the large gain of PRA, the behavior-related information needs to be predicted, say user trajectory,

traffic load, average channel gains or data rates, which change in different timescales since the dynamics are

induced by different mechanisms. Despite that various behavior-related information has been predicted in the

literature, they are targeted to diverse applications such as location-based service and autonomous driving. As a

result, existing prediction methods either provide coarse-grained prediction over long horizon (e.g., one day) with

data of low sampling resolution (e.g., with sampling period of 15 minutes or even a hour), or support fine-grained

prediction over short horizon (e.g., few seconds) with data of high resolution (e.g., with sampling period of 10~100

milliseconds). Such long or short prediction horizon is useless for PRA, and recording fine-grained data in cellular

network needs huge storage capacity. Furthermore, it is well-known that prediction accuracy deteriorates quickly as

the prediction horizon increases. Yet few priori works have addressed if the required information is predictable

over the minute-level horizon, and how the required information for predictive resource allocation is predicted. In

[9], a hierarchical PRA scheme for NRT service was proposed, which only requires coarse-grained prediction. With

such a scheme, high throughput can be supported by predicting the required information with heterogeneous data

with low-resolution, and with much less training samples than the existing scheme. Considering that machine

learning is a powerful tool to predict user behavior and harness the vast amount of data measured in cellular

networks, the study in [9] further investigated if the required knowledge for making the decision can has coarse

temporal-spatial resolution and can be predicted from raw data in low resolution without any translation. A deep

neural network (DNN) was designed to learn the future information required for making decision directly from

different types of past data with different resolution observable in cellular networks. The results show that the

proposed scheme with the end-to-end (E2E) prediction performs closely to the relevant optimal solution with

perfect prediction, and provides dramatic gain over non-predictive counterpart in supporting high request arrival

rate for NRT service. To avoid offline training and allow online learning, deep reinforcement learning policy was

proposed in [10] for PRA, by taking energy efficiency as an example objective.

In interference networks, PRA also provides huge performance gain. Intuitively, interference can be controlled

more flexibly by leveraging future average channel gains to improve QoS of mobile users with delay tolerant

service, since the interference can be coordinated in a much larger time-space range. However, optimizing the

resource allocation in a predictive manner for interference networks is challenging, since how to allocate future


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resources depends on the future signal to interference plus-noise ratio (SINR), and the SINR relies on the assigned

resources. As a result, most existing works for PRA ignore inter-cell interference (ICI) or simply treating ICI as

noise, which limits the user experience and network performance in densely-deployed cellular networks. In [11], a

policy of PRA with ICI coordination (PRA-ICIC) was proposed, which employs the future average channel gains

of each user in a prediction window to coordinate ICI by muting base stations (BSs) and optimizing user

association. However, finding the optimal solution of PRA-ICIC incurs prohibitive computational complexity,

which is unaffordable for a longer prediction window. To reduce the complexity of real-time processing for

optimizing transceivers, a DNN was developed in [12] to learn the optimal policy for heterogeneous networks. The

DNN can learn from the historical data in an end-to-end manner. Simulation results show that the proposed

DNN-based solution performs closely to the numerically-obtained optimal solution with much lower computational

complexity.

(2) Solutions

a) Coarse-grained PRA with E2E prediction

In [9], it shows that PRA with minute- and cell-level knowledge prediction is able to achieve negligible

performance loss from the optimal solution with perfect future rate in each frame of a prediction window. A

multi-input-multi-output orthogonal frequency multi-access (MIMO-OFDMA) was taken as an example system

and throughput as an example performance metric. A threshold-based scheme was proposed to achieve high

throughput while satisfying the QoS for the MSs requesting video-on-demand (VoD) service, based on the idea

proposed in [14]. The principle behind PRA for serving NRT users is to transmit more data to a MS when the MS

moves to a location with high average data rate, whose average rate depends on its average channel gain and the

traffic load in its located cell. In order to improve the performance of network with guaranteed QoS for the MS,

such a principle can be interpreted as finding two thresholds. To support high throughput, the BSs should transmit

more bits to a MS with good channel condition. Hence, a threshold is necessary to determine whether or not the

average channel gain at a location is good. To reduce stalling time in video streaming, more segments should be

transmitted by a BS with light traffic load to a MS heading to heavily loaded cells. Hence, another threshold is

required to determine whether or not the residual resource in a cell is sufficient for causing less stalling. With the

two thresholds, a BS can judge if a MS can be served with higher average rate. Such a scheme allows the decision

of resource management to be made in a central processor (CP) and the BSs in different timescales, and allows the

knowledge to be predicted with less training samples.


25

To implement such a threshold-based PRA, the CP needs to first predict the information based on historical

data in the observation window. Then, the CP translates the predicted information to the required knowledge for

making decision. For example, the CP first predicts trajectory and then translates it into future average gains with

the radio map and finally computes the threshold. The whole procedure to obtain the finally predicted knowledge is

rather complicated in this way. Inspired by the ability of DNN in learning the hidden features of the inputs and in

deducing complicated relation between different information, a DNN was designed in [9] to predict the required

knowledge for decision making in an E2E manner, where the procedure of predicting the required knowledge only

consists of a single learning step, without the need for multi-stage learning pipelines and the information

translation.

The designed DNN is shown in Figure 2.16, which contains four sub-DNNs to predict different information.

Each sub-DNN is a fully connected (FC) DNN, which contains L layers. The input of the DNN is the historical data,

and the output is the predicted two thresholds as well as the service BS set. Sub-DNN-1 is used to predict the

service BS set, i.e., the BSs that the typical MS (say MSk ) will successively associate with, which depends on the

trajectory of the MS. The location of the MS can be implicitly inferred at the CP with the average channel gains of

the MS from several adjacent BSs and the known locations of the BSs. Therefore, the input of sub-DNN-1 is the

number of records for the past average channel gains. The output is the cells that each MS will associate with in the

prediction window. Sub-DNN-2 is used to predict the threshold for average channel gains. Since the threshold

depends on user’s trajectory, the input is the same as that in Sub-DNN-1. Sub-DNN-3 is used to predict the average

residual bandwidth of the typical BS, which depends on the resource occupied by the RT service. Therefore, the

input is the number of records of RT traffic load. Sub-DNN-4 is used to predict the threshold for residual

bandwidth, which is related to the past VoD requests and past sojourn time of the MSs. The input is the numbers of

records of VoD requests and sojourn time.


26

Figure 2.16 Structure of the designed DNN for the end-to-end prediction.

Figure 2.17. Observation window and Prediction window.

As shown in Figure 2.17, the input of the DNN is from the collected historical data records in the observation

window, and the output is the predicted knowledge. The sub-DNNs can predict different knowledge from different

types of record in the prediction window.

b) PRA-ICIC with E2E prediction

The study in [10] investigated predictive resource allocation for mobile users requesting NRT service in

HetNets with ICI. To coordinate interference, a BS muting is allowed when the BS generating strong ICI. To deal

with the “chicken-and-egg” challenge, the resource allocation plan is designed in two steps at the start of a

prediction window, when future average channel gains are available. A predictive interference coordination scheme

is optimized to find which BSs can transmit to which users concurrently that ensures a given average SINR. Then, a


27

common resource allocation plan is optimized for this group of BS-user pairs that maximizes a network utility

aiming to improve user satisfactory rate. Since the common plan is made for a group of users that request files with

different sizes and are with different average SINRs, it is non-trivial to satisfy the QoS for all these users

meanwhile not to waste radio resources. To deal with the difficulty in making a single plan for multiple users, a

logistic function is introduced to characterize the network utility that can make more users satisfied without wasting

radio resources. By resorting to graph theory, we obtain the optimal interference coordination scheme and the

optimal resource allocation plan. Simulation results showed remarkable performance gain over the non-predictive

ICI-coordination or predictive resource allocation designed by assuming ICI as noise.

To reduce the complexity of real-time processing for optimizing transceivers in wireless systems, an

off-line-trained DNN was introduced to approximate the relation between the optimized transceiver and channel

gains [11,12]. Such an approach was employed in [12] to learn the optimal PRA policy, where the on-line

complexity is reduced to about 1% of the traditional numerical optimization algorithm (i.e., interior point method).

To design a DNN to learn the PRA-ICIC policy in [10], the study in [11] finds that in this problem there exists data

bias issue, where the optimal solution contains much more “0” than “1”. When such solutions are used as the labels

for supervised learning, the trained DNN cannot learn the optimal policy very well. To circumvent this problem,

the DNN was designed to learn whether each user is served in each frame of the prediction window rather than

learn the optimal policy directly. Then, a user association algorithm was designed to determine which BSs transmit

signals to which users in each frame.

As shown in Figure 2.18, the DNN is aimed to learn whether or not the UEs are served in each frame, which is

designed as a FC-DNN. In the interference networks, each UE is interfered by other BSs when they are serving

other UEs, which affects resource allocation. Consequently, the input of the DNN should contain the information of

all BSs and all UEs within the prediction window. In particular, the input should contain two types of known

parameters used in the PRA-ICIC policy. The first type is the maximal average receive power of all MSs from all

BSs within the prediction window, the second type is the normalized data amount of all MSs from all BSs. The

expected output is whether each MS is served in each frame of the prediction window.

The PRA-ICIC policy is implemented by three steps, where the CP operates in the following three steps. (i)

Predicts the maximal average receive power and normalized data amount in the prediction window with the

historically recorded values in the observation window. (ii) Learn an intermediate solution of the policy by the

DNN. (iii) Use the association algorithm to obtain the resource allocation plans. In practice, the values of maximal

average receive power and normalized data amount can also be predicted by DNN, which can be incorporated into


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the DNN for learning the optimal policy. Hence, a DNN can be designed to implement the policy in an E2E manner,

where the DNN directly learns the policy by implicitly predicting the future information such that step (i) is omitted.

Specifically, the input of the DNN is the historically recorded values in an observation window, and the output is

the same as the previous DNN.

Figure 2.18. Structure of the designed DNN for the PRA-ICIC.


(a) with random stopping (b) without random stopping

Figure. 2.19 Performance of coarse-grained PRA

…

…

DNN

𝒙1

x

𝒚1

…

…

…

: Fully Connected

Input layer Hidden layers Output layer

…

𝒙2

𝒙3

𝒙𝑻𝒇

𝒚2

𝒚𝑻𝒇

y …


29

Figure. 2.19 shows the performance of coarse-grained PRA. Figure. 2.19 (a) and (b) respectively illustrate the

simulation results of the supported maximal arrival rate of MSs versus the total stalling time allowed by each MS,

which reflects the ability of each method to support the system throughput. The “optimal method” is the upper

bound of performance that is achieved by solving the optimization problem when the prediction information is

accurate, and the “benchmark method” is the traditional non-predictive method. The simulation is carried out in

two scenarios of high and low network residual resources. Simulation results showed that the proposed scheme

with E2E prediction performs closely to the relevant optimal solution with perfect prediction, and provides

dramatic gain over non-predictive counterpart in supporting high request arrival rate for the NRT service. The gain

grows with the required duration of the prediction window.

(a) T = 5 s. (a) T = 30 s.

Figure. 2.20 Performance of PRA-ICIC

Figure. 2.20 shows the performance of PRA-ICIC. Compared with the non-PPA with ICIC and PRA without

ICIC, the PRA-ICIC outperforms the reference strategies for different values of B and K. It provides much higher

user satisfactory rate than existing PRA policies when the traffic load is high and outperforms non-predictive

resource allocation remarkably when the prediction window is long.

The “DNN-non-E2E” is the DNN trained in a non-E2E manner, where the input is the predicted information

in the prediction window and the “DNN-E2E” is the DNN trained in the E2E manner, where the input is the

historical information in the observation window. Simulation results showed that the proposed DNN with E2E

learning performs closely to the optimal policy, but only with 6‰ of the computational time of the original

problem.


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3.4.4 Use case 9: Big Data Analysis for Handover Optimization in Massive-MIMO Cellular

Systems

(1) Description

The application of massive multiple-input multiple-output (MIMO) technology, relying on a large number of

antennas and transceivers, is able to greatly increase system capacity, extend cell coverage and reduce interference

level. One of the key issues in massive-MIMO is the beam selection for each served user equipment (UE),

especially in the handover procedure in the cellular networks. The handover success rate and interruption time are

dependent on the target beam selection effect and speed. In general, the target base station has no a priori

hypothesis about the channel information of the incoming UE until UE sending measurement report or wait for UE

sending SRS and then calculate the beam that fits to uplink and downlink by channel reciprocity. Both of these two

approaches would cause significant handover delay and service interruption in the scale of milliseconds or longer.

A self-optimization network (SON)-type approach, named as enhanced automatic neighbor relation (eANR)

function, was adopted to solve this issue, which extends the traditional neighbor cell relation (NCR) table in ANR

function with massive MIMO information by dynamic updating the suitable beam pairing relations between source

cell and target cell. Based on it, when a UE is about to perform handover, the suitable target-cell beam can be

derived by simply looking up beam-specific NCR table with the source-cell beam as index, without taking time in

field measurement on the radio channel of target cells, and hence the handover process in massive-MIMO cellular

systems can be accelerated. We further develops such SON-type approach employing the big data analysis on beam

spectrum storages to replace the pairing table of one-to-one source-target beams in the issue of handover

optimization [15].

(2) Proposed methods

Assuming on each subcarrier, the complete channel response vector ℎ⃗ 𝑘(𝑐)

equals the Kronecher product of the

horizontal vector ℎ⃗ 𝑘(ℎ)

and vertical vector ℎ⃗ 𝑘(𝑣)

,

ℎ⃗ 𝑘(𝑐)

= ℎ⃗ 𝑘(ℎ)

⨂ℎ⃗ 𝑘(𝑣)

, 𝑘 = 0,… , 𝐾 − 1. (1)

When scanning the beam codebook with user channel response vector, we can project these multi-path radio

channel profiles onto the subspace spanned by the beam codeword dimensions, so as to generate a beam spectrum,

denoted as 𝐶 (ℎ) ≜ [𝑐0, 𝑐1, ⋯ 𝑐𝑆ℎ−1] in the horizontal plane or 𝐶 (𝑣) ≜ [𝑐0′ , 𝑐1

′ , ⋯ 𝑐𝑆𝑣−1′ ] in the vertical plane, where


31

𝑐𝑥 =1

𝐾∑

|⟨�⃗� 𝑥,ℎ⃗⃗ 𝑘(ℎ)

⟩|

‖�⃗� 𝑥‖2‖ℎ⃗⃗ 𝑘

(ℎ)‖

2

𝐾−1𝑘=0 , 𝑥 = 0,… , 𝑆ℎ − 1, (2)

𝑐𝑦′ =

1

𝐾∑

|⟨�⃗� 𝑦,ℎ⃗⃗ 𝑘(𝑣)

⟩|

‖�⃗� 𝑦‖2‖ℎ⃗⃗

𝑘(𝑣)

‖2

𝐾−1𝑘=0 , 𝑦 = 0,… , 𝑆𝑣 − 1, (3)

The handover process with big data analysis is shown in Figure. 2.21, where the beam spectrum handling to promote

handover includes two processes: beam spectrum acquisition (big data storage and management) and beam spectrum

utilization (big data processing), and each of them can be further divided into two steps.

(a). Block diagram of beam spectrum acquisition and utilization processing (b). Operations in handover procedure

Figure. 2.21 Diagrams of the proposed methold for handover optimization

The system works in the training phase before the big data storage with sufficient records are accumulated.

Step 1 is to acquire the source-cell beam spectrum and the target-cell beam index by the source base station to build

or update the beam spectrum storage. Since the user beam differentiation at inter-site handover region mainly

occurs in the horizontal plane. Therefore, in order to reduce the complexity, here we only retain the

horizontal-plane beam spectrum 𝐶𝑠𝑜𝑢𝑟𝑐𝑒(ℎ)

in the source cell, but replace the vertical plane beam spectrum 𝐶𝑠𝑜𝑢𝑟𝑐𝑒(𝑣)

by a single dominant vertical plane dominant beam index �̅�𝑠𝑜𝑢𝑟𝑐𝑒 whose corresponding beam has the highest

beamforming gain. They can be derived from the calculation of (2) and (4) by source base station based on uplink SRS,

i.e. operation A in Figure. 2.21,

�̅�𝑠𝑜𝑢𝑟𝑐𝑒 = 𝑎𝑟𝑔 max𝑦

∑ |⟨�⃗� 𝑦, ℎ⃗ 𝑠𝑜𝑢𝑟𝑐𝑒,𝑘(𝑣) ⟩|𝐾−1

𝑘=0 . (4)

Next, the suitable target-cell beam indexes can be derived by two alternative approaches: a) the cell-edge UE is

commanded to make measurement of the suitable beam of target cell and report to the source base station, before it


32

performs handover, i.e. operation B-D; b) after the UE completes handover, the target base station acquires the

suitable beam in a regular way and then indicates the target beam to the source base station via inter-base station

message channel (like X2 interface), i.e. operation L. Based on the channel response vector, the suitable target-cell

beam index pair for horizontal plane and vertical plane is determined by calculating its Hermitian inner-product

with candidate beam codeword,

(�̅�𝑡𝑎𝑟𝑔𝑒𝑡 , �̅�𝑡𝑎𝑟𝑔𝑒𝑡) = 𝑎𝑟𝑔max𝑥,𝑦

∑ |⟨�⃗� 𝑥 ⊗ �⃗� 𝑦, ℎ⃗ 𝑡𝑎𝑟𝑔𝑒𝑡,𝑘(𝑐) ⟩|𝐾−1

𝑘=0 . (5)

In Step 2, the instant horizontal-plane beam spectrum and vertical-plane dominant beam index in the source

cell and the suitable horizontal and vertical beam indexes in the target cell, together with the time stamp 𝑇𝑙, are

integrated as a new record ℛ𝑙,

ℛ𝑙 ≜ [𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,𝑙(ℎ)

, �̅�𝑠𝑜𝑢𝑟𝑐𝑒,𝑙 , �̅�𝑡𝑎𝑟𝑔𝑒𝑡,𝑙 , �̅�𝑡𝑎𝑟𝑔𝑒𝑡,𝑙 , 𝑇𝑙], (6)

and then added into the beam spectrum storage.

To reduce the complexity, the big data storage can be managed in the way of dividing into a number of

sub-storage, each of which corresponds to one of the source vertical-plane beam indexes,

𝕊𝑢 ≜ {ℛ𝑙|�̅�𝑠𝑜𝑢𝑟𝑐𝑒,𝑙 = 𝑢}. (7)

The system works in the execution phase after the big-data storage with sufficient records accumulated. In

Step 3, the source base station would determine the suitable target-cell post-handover beam (�̅�𝑡𝑎𝑟𝑔𝑒𝑡, �̅�𝑡𝑎𝑟𝑔𝑒𝑡)

based on the known current horizontal-plane beam spectrum 𝐶 𝑠𝑜𝑢𝑟𝑐𝑒(ℎ)

and vertical-plane dominant beam index

�̅�𝑠𝑜𝑢𝑟𝑐𝑒 in source cell, by big data processing on the beam spectrum records, i.e. operation E. Next, the source base

station indicates the determined target-cell beam to the target base station, i.e. operation F. The source-cell

vertical-plane dominant beam index of current user (denoted as �̅�𝑠𝑜𝑢𝑟𝑐𝑒,0) is used to address a correspondent sub-storage

𝕊�̅�𝑠𝑜𝑢𝑟𝑐𝑒,0. In following, the matching degrees (denoted as 𝑀𝑙) between the source-cell horizontal-plane of the current

user (denoted as 𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,0(ℎ)

) and the stored horizontal-plane beam spectrums of historical users (denoted as 𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,𝑙(ℎ)

) , are

calculated,

𝑀𝑙 ≜|⟨𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,0

(ℎ),𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,𝑙

(ℎ)⟩|

‖𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,0(ℎ)

‖2‖𝐶 𝑠𝑜𝑢𝑟𝑐𝑒,𝑙

(ℎ)‖

2

, ∀𝑙, with ℛ𝑙 ∈ 𝕊�̅�𝑠𝑜𝑢𝑟𝑐𝑒,0. (8)

Second, the target-cell beam index is jointly determined based on 𝑀𝑙 and 𝑇𝑙 of the records. Set a sliding

window to filter out a certain number (denoted as L) of the latest beam spectrums records from the corresponding

sub-storage 𝕊�̅�𝑠𝑜𝑢𝑟𝑐𝑒,0, whose matching degree is not less than a pre-configured threshold 𝛼, 0 < 𝛼 < 1, i.e. the

latest records with �̅�𝑠𝑜𝑢𝑟𝑐𝑒,𝑙 = �̅�𝑠𝑜𝑢𝑟𝑐𝑒,0 and 𝑀𝑙 ≥ 𝛼; Tag a forgetting weight to each record whose value depends


33

on the timing gap between the recorded time stamp and the present time stamp, 𝜔𝑙 ≜ 𝛽𝑇0−𝑇𝑙wl , 0 < 𝛽 < 1,

where 𝛽 is a pre-configured forgetting factor. In this way, the matching degree is updated as

𝑀𝑙′ = 𝑀𝑙𝜔𝑙; (9)

Generate the target beam index of the current user (�̅�𝑡𝑎𝑟𝑔𝑒𝑡,0, �̅�𝑡𝑎𝑟𝑔𝑒𝑡,0) from the beam codeword that is the

nearest to the weighted average of filtered records,

(�̅�𝑡𝑎𝑟𝑔𝑒𝑡,0, �̅�𝑡𝑎𝑟𝑔𝑒𝑡,0) = ⌊∑ 𝑀𝑙

′∙(�̅�𝑡𝑎𝑟𝑔𝑒𝑡,𝑙,�̅�𝑡𝑎𝑟𝑔𝑒𝑡,𝑙)𝐿𝑙=1

∑ 𝑀𝑙′𝐿

𝑙=1⌉. (10)

In Step 4, dependent on the indicated beam information, the target base station can prepare a dedicated

transmitting or receiving beam for the incoming handover UE, i.e. operations from G to K. In order to guarantee the

freshness of big data storage, after handover, the target base station updates the actual-used target-cell beam and

feeds back to the source base station, with which a new beam spectrum record ℛ𝑙 is recorded as in Step 2, i.e.

operation L. This action is not associated to the handover process and can be taken in spare time after handover is

completed, so it does not impact handover latency. It should be noted that the big data storage is real-time updated

and the time stamp in the record would influence the result of target beam selection, so this function can be

classified as autonomous SON function.

(3) Performance evaluation

To evaluate the performance of the proposed beam spectrum-based target-cell beam determination method, for

simplicity but without loss of generality, a simulation scenario with two neighboring massive-MIMO cells is

constructed, whose parameters are given in the following table.

Table. 2.4: Simulation parameters

A number of 100 UEs are distributed randomly in the coverage of these two cells, and they move

independently and continuously with pedestrian speed (5km/h). When the handover triggering conditions are

satisfied, the UE would perform handover from its original cell to the other. To emulate the environment variation,

one random-selected scattering cluster is moved to another arbitrary location every 1000 seconds. Upon handover,

the metric of target-cell beamforming gain corresponding to the determined wide-band target-cell beam, denoted as


34

𝑔𝑡𝑎𝑟𝑔𝑒𝑡,0, is recorded,

𝑔𝑡𝑎𝑟𝑔𝑒𝑡,0 ≜𝑆ℎ𝑆𝑣

𝐾∑

|⟨(�⃗� �̅�𝑡𝑎𝑟𝑔𝑒𝑡,0⊗�⃗� �̅�𝑡𝑎𝑟𝑔𝑒𝑡,0),ℎ⃗⃗ 𝑘(𝑐)

⟩|

‖�⃗� �̅�𝑡𝑎𝑟𝑔𝑒𝑡,0⊗�⃗� �̅�𝑡𝑎𝑟𝑔𝑒𝑡,0‖2‖ℎ⃗⃗ 𝑘

(𝑐)‖

2

𝐾−1𝑘=0 . (13)

In a sufficiently long period, the cumulative distribution function (cdf) curve of this metric can be drawn as

shown in Figure. 2.22 left part, the cdf curve of beamforming gain by the current BS-based method for Umi-NLOS

channels is present. The number of referenced records L in (10) equals 100. Wide-band beam spectrum is employed.

The algorithm parameters 𝛼 = 0.99 and 𝛽 = 0.9. From the curves, 80% beamforming gains by the BS-based

method are higher than 18.4 dB, 50% are higher than 19.6 dB.

Figure. 2.22 The cdf curve on beamforming gain for Umi-NLOS channels (left) and Umi-LOS channels (right)

The curve by the prior-art eANR method and the curve by the field measurement on target beam are also drawn

for comparison. Obviously, for the SON-type methods, either eANR method or BS-based method, the target-cell

beamforming gain would have a certain loss compared with the field measurement. However, such beamforming

gain loss is sacrificed for the benefit of faster handover process and shorter service interruption. At 50th-percentile

of cdf curve, the progress from eANR method to BS-based method is that the target-cell beamforming gain

performance loss compared with the field measurement decreases from 0.6 dB to 0.3 dB, i.e. from 12% loss to 6%

loss, which is about 50% reduction. Figure. 2.22 right part present the performance in Umi-LOS channel. It can be

found that the benefit of the BS-based method over eANR method becomes weakened: only 0.1 dB difference at

50th-percentile of cdf curve.


35

3.5 Radio Transmission Technologies

3.5.1 Use case 10:Deep learning aided codebook design for grant-free NOMA

(1) Description

Grant-free non-orthogonal multiple access (NOMA), which exploits the joint benefit of grant-less access

mechanism and non-orthogonal signal superposition, has been considered to simultaneously realize low latency and

high-efficient massive access. In grant-free NOMA, data transmissions are autonomously activated by users

without explicit dynamic grant, which greatly reduces the control/user plane latency caused by signaling interaction

and scheduling grant. However, due to the decentralized instant transmissions in grant-free NOMA, the activity

information is unknown at the receiver. Moreover, the unexpected inter-user interference leads to the deteriorated

transmission reliability.

Unlink grant-based NOMA being designed following the classical multi-user information theory, grant-free

NOMA does not obey the conventional Shannon information theory and is usually designed empirically. For

example, at the transmitter side, grant-free mechanism is directly introduced into the state-of-art grant-based

NOMA schemes. At the receiver side, some resort to compressive sensing-based approaches, and some consider

the message passing algorithm-based methods. Although existing schemes have significantly contributed to the low

latency multi-user radio access, few have focused on the end-to-end optimization of the entire grant-free NOMA

system. The major obstacle lays in the fact that the introduction of random user activation behavior and

non-orthogonal transmissions makes the grant-free NOMA non-trivial to be mathematically modeled and optimized

by traditional optimization methods. To this end, this work focuses on the end-to-end optimization of grant-free

NOMA based on deep learning (DL) [16].

(2) Solutions

Consider a typical uplink grant-free NOMA system with L users and a base station (BS), where each user may

activate and transmit signals in each time interval with probability α, 0 <α ≤ 1. The activation probability can

either be acquired according to the traffic models of users, or be estimated by the BS according to the historical

data. The source bits of each user are first modulated and then mapped to a symbol sequence according to

user-specific symbol spreading signature. The receiver jointly detects the user activity as well as source bits as

shown in Figure. 2.23.


36

Figure. 2.23: System model of Grant-free NOMA.

To couple with the system model of grant-free NOMA, a specially designed end-to-end network model based

on deep variational auto-encoder (VAE) is considered to parameterize the proposed variational optimization

problem, which consists of a NOMA encoding network and a NOMA decoding network as shown in Figure. 2.24.

NOMA encoding network models the random user activation and symbol spreading. Different from the

conventional VAE which employs a fully-connected neural network at the encoder, the proposed encoding network

of DL-aided grant-free NOMA is composed by L isolated sub-networks, since the users cannot obtain external

information from other users before transmission.

NOMA decoding network jointly estimates the user activity and the transmitted symbols. In uplink NOMA,

the receiver aims to jointly detect L signal streams. Therefore, a fully-connected neural network can be deployed as

the probabilistic decoder. Due to the grant-free access mechanism, the receiver shall also identify the user activity.

Intuitively, the reconstructed symbols hold the information about whether a user is activated. To this end, we just

add a single layer with Bernoulli output after the decoder to detect the user activity with ultra-low complexity.

Figure. 2.24: Detailed network structure

To achieve detection accuracy gain over conventional grant-free NOMA, the key is to properly train this

network. The loss function compares the differences between estimated output and real output, whose design

greatly affects the performance of deep learning. A novel multi-loss function is introduced to train the proposed

network, which includes a symbol reconstruction loss and a user activity detection loss. Specifically, the prior

information of user’s activation probability is exploited by introducing the confidence penalty in the loss function.


37

The spreading signatures and receiving algorithm can be obtained according to the training results of NOMA

encoding and decoding networks, respectively.


In this section, experiments and Monte-Carlo simulations are conducted to validate the performance gain of the

proposed DL-aided grant-free NOMA. Both linear and non-linear spreading signatures are derived by training the

proposed network, which show remarkable gain on the transmission reliability than conventional grant-free NOMA

schemes.

Non-linear symbol spreading signatures, i.e., different modulation symbols correspond to different spreading

signatures, can be illustrated by multi-dimensional constellations where each source message corresponds to a

multi-dimensional constellation point. Figure. 2.25 illustrates some examples of the learned multi-dimensional

constellations with various message sizes and activation probabilities. First of all, we can observe that constellation

projection operation and sparse RE mapping manner are naturally learned in the proposed scheme, which can

reduce the receiving complexity. Besides, the learned constellation tends to transmit signals with various power

levels in 4 Res, which inherently introduce power gain differences and can facilitate the interference cancellation.

These observations show that DL-aided grant-free NOMA automatically tap into some physical insights on the

design of spreading signature, which conventionally requires expert knowledge in wireless communications.

Figure. 2.25: Examples of the learned multi-dimensional constellations of one user in 4 Res with different modulation order M. In each

example, the 4 points with the same color and shape in 4 Res constitute a four-dimensional constellation point.

While multi-dimensional constellation normally requires iterative-based detection methods which dissatisfy

the low latency services, we apply a linear constraint to derive linear spreading signatures for grant-free NOMA,

where all modulation symbols of a user have the same spreading signature. This helps to implement simple

detectors, e.g. match-filter (MF) detection. With the setting of UE number L=6 and spreading factor K=4, a


38

randomly generated example of the learned spreading signature set is shown above, where the l-th column is the

normalized spreading signature of user-l.

Figure. 2.26: SER vs SNR with various activation probabilities Figure. 2.27: SER performance of the proposed scheme

Symbol error rate (SER) of the proposed scheme is evaluated in Figure. 2.26 and Figure. 2.27. As shown in

Figure. 2.26, we first compare the achievable SER of the linear spreading signatures generated by the proposed

scheme with state-of-art grant-free NOMA schemes, i.e., welch-bound equality (WBE) sequences and sequences of

multi-user shared access (MUSA), under a simple MF receiver. It is observed that SER reduction can be achieved

compared to conventional grant-free NOMA while not introducing any increase in receiving complexity or latency.

We then employ the DNN-based receiver (DNNrx), which constitutes a near maximum-likelihood (ML) receiver

and is trained along with the linear spreading signatures. In this case, 10-3 SER can be achieved with various

activation probabilities, which is promising to satisfy the rigorous requirement on the reliability.

SER performance of the proposed scheme with various M is shown in Figure. 2.27. It is observed that

ultra-high reliability is achieved with various M, which validates the universality of the proposed scheme. Besides,

we observe that multi-dimensional signatures achieve slightly better SER performance than linear signatures with

DNNrx. This is because the multi-dimensional signatures are more flexible than its linear counterparts. However,

maximum-likelihood based detection algorithms are required to distinguish multiple data streams with

multi-dimensional signatures, which leads to large detection delay and high complexity. On the contrary, the

overlapped signals with linear spreading signatures can be quickly separated even with a very simple receiver, e.g.

MF receiver as shown in Figure. 2.26.

3.6 Discussion on the wireless big data and AI/ML algorithms for wireless network

applications

In [2], the wireless big data and AI/ML algorithms are presented in Table 1, which gives a useful mapping between


39

the use cases and the potential AI algorithms along with the required wireless big data. In this section, we aims to

provide more insights for the future works on the algorithms and requited data via updating the table based on the

recent progress presented in this whitepaper and also that in the series of whitepapers [1-3].

It can be seen from the Table. 2.5, various well known learning algorithms are widely utilized in the wireless

network use cases. However, most of them are designed originally for other domains, e.g., the image processing or

nature language processing, which is obviously not optimized for the wireless network. How to leverage the

wireless domain specific information and existing wireless communication theory and models to design and further

improve the intelligent algorithms suitable for wireless network application still needs further study.

Table 2.5 Wireless Big Data and AI/data analytics for wireless Network Applications

Use Cases AI Algorithms Related Wireless Big Data

Intelligent

Network

Management and

Orchestration

Coverage Hole Discovery K-means,

Supervised Learning

Call trace, UE measurement

report (MR), location, radio

link failure, etc.

QoE Issue Debugging Decision Tree

Control plane signaling,

user plane data, webpage

downloading time, round

trip time, delay, jitter,

packet loss rate, etc.

Network Energy Efficiency

Optimization Neural Network

MR, cell performance

metric (PM), network

configuration parameters

(CM) and user xDR data

Coverage and Capacity

Optimization Deep Q Learning MR, PM, CM, etc.

KPI Anomaly Detection Clustering, Autoencoder,

Decision Tree

Fault data, PM counter,

CM, etc.

KPI Anomaly Diagnosis

Clustering, e.g.,

K-means; isolate Forest;

Rule based

Fault data, PM counter,

CM, etc.

Smart Carrier Licence Resource

Scheduling CNN+LSTM, DNN

KPI counters, e.g., UL/DL

PRB usage, PDCP

throughput,

Geo Data Enhanced Coverage

Estimation and Planning

Statistical data analytics

and data visualization

GNSS, Finger-printing,

TDOA, RSRP/RSRQ,

network geography

information

Intelligent MEC

Proactive Cache

Collaborative Filtering,

Deep/Reinforcement

Learning (RL)

User XDR data that can

reflect user request behavior

and mobility pattern etc.

AI powered QoE Cross-Layer

Optimization Neural network

PDCP buffer, base station

load, MAC rate, service

type, packet error rate, etc.

AI Based Positioning and

Localization DenseNet, CNN, DNN

channel impulse response

Amplitude and phase

Intelligent Radio Service Type Recognition Neural Network, CNN, User XDR data that reflects


40

4 Intent Aware Wireless Network

4.1 Motivation

Recently, the AI technologies such as machine learning and big data analytics are developing very quickly, and

they are also applied in wireless networks to reduce human labor and improve network performance. Intent aware

network management is one of most important applications of those technologies.

Compared to existing human-oriented network management, intent aware network management uses simple

instructions to tell the network “what to do” rather than “how to do” to improve the network automation capability

and reduce errors caused by manual decision making. Moreover, in the scenarios of the vertical industry, it is

Resource

Optimization

Naïve Bayes, SVM, etc. service pattern

Resource Management for

Network Slicing DQN, GAN-DDQN

MR, Number of arrived

packets in the window time,

SE, QoE performance,

allocated per slice resources

etc. s

Customized Mobility

Management

Neural Network, Deep

Reinforcement Learning,

SVM

MR, PM, CM, load, user

XDR data that reflects user

mobility pattern

Big Data Analysis for Handover

Optimization in Massive-MIMO

Cellular Systems

SVM, DNN, beam

spectrum storage

RSRP, RSRQ, beam info,

user handover signaling

logs and performance, etc

Intelligent Admission control &

Proactive Resource Allocation Reinforcement Learning

MR, performance metrics,

user XDR data that reflects

user mobility pattern

User Portraits assisted User

Scheduling SVM, KNN, etc.

MR, terminal type, chip

type, transmission capacity ,

service type, buffer length

Virtual Grid Enabled Network

Performance Optimization Neural Network

History KPI statistics, MR,

etc

User Time-Frequency Resource

Scheduling

Deep Neural Network,

Reinforcement Learning

Quality of service (QoS),

channel quality indicator

(CQI), guaranteed bit rate

(GBR), RLC buffer size,

historical rate, service type,

resources

Intelligent Radio

Transmission

Technologies

Wireless Big Data Assisted

Channel Modeling Neural Network, PCA Channel measurement data

OFDM/MIMO Detection Deep Learning Channel information,

transmit/receive data

AMC

/Rank Indicator (RI)

Optimization

Reinforcement Learning,

Neural Network,

Random Forest

Signal to interference and

noise ratio (SINR), CQI,

ACK/NACK, etc.

Codebook design for grant-free

NOMA

deep variational

auto-encoder (VAE)

User transmitted signals,

user activation probability


41

important that the intent aware network management can help the vertical industry customers manage the network

without getting into the detail of network since the vertical industry customers may not have sufficient network

operation and maintenance knowledge.

Currently, the northbound interface (Itf-N) defined in 3GPP SA5 is based on the network resource model (NRM),

which is intended to model all the network resources of the managed objects in the wireless network. So that,

Network Management System (NMS) can directly add, delete and change all management objects through

Configure Management (CM), Performance Management (PM), Fault Management (FM) and other types of

operations, which relies heavily on expert knowledge and experiences. Since wireless networking products have

thousands of parameters that can be used for wireless resource management and user service quality assurance

management, it is very difficult to conduct the optimal solutions and avoid the errors during the network

management manually. In addition, different vendors may have a lot of proprietary implementations for

competitive differentiation, which further increase the difficulty of the network OAM under multi-vendor

interoperability scenarios.

The intent aware network uses the intent model to express the network management requirements, which only

involves the purpose description and the general term of the domain. The intent system is used to map the

standardized intent model to the vendor-specific network management task to achieve multi-vendor interoperability.

Then, the automation mechanisms, e.g., data-driven technologies, data analytics and AI/ML can be applied to

accomplish the translated network management tasks. Figure 3.1 illustrates the evolution from NRM based

management to intent aware/driven management.


42

Figure 3.1 Evolution from NRM based management to Intent driven management

4.2 Use Cases for intent aware wireless network

4.2.1 Use Case 1: Intent driven network service provision

The service provider expresses his intent for a network by providing high level requirements such as the service

type to be supported with corresponding geographical areas and time durations, maximum number of UEs, DL/UL

throughput per UE, etc. to the intent system of the network operator. The service provider does not care about what

network elements are used and how to connect those network elements.

The intent system translates the network deployment intent for the specific area to the network deployment related

requirements (e.g., using network slice or not, network topologies, etc.) and the configurations (e.g. DL/UL

throughput per cell and UE measurements) for the management network equipment in this specific area. According

to these network related requirements, the network uses the network provisioning procedures (e.g., network slice

instance creation) to provide required network instance and communication service for the specific UE, and

continuously monitor the managed network equipment to ensure the intent.

Figure 3.2 intent driven network service provision

4.2.2 Use Case 2: Intent driven network provision and assurance for 5G vertical services

Vertical customers are becoming increasingly important for operators in 5G era. It is crucial for operators to

provide a service for vertical users, where they can customize or select service blueprints from a predefined


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catalogue, configure and instantiate, operate and monitor their network status for their vertical services. It worth

noting that the vertical customers may not have a detailed knowledge about the network. Therefore, the intent

approach allows the vertical users to focus only on the business aspects of their services (e.g., how many users need

to be supported with what kind of throughput, latency and mobility performance assurance, and/or the level of

required security). The network operator can translates the vertical’s demands into a number of network specific

features and requirements and identifies the best combination of the network features and optimization parameters

to deliver the requested services. If the network slicing is supported, the network operator (intent producer) can use

the networks slice requirements defined in the [17] to create or reconfigure the network slice instance. If the

network slicing is not supported, the network operator can derives the network requirement and provides the

requirements via consuming the intent/policy service provided by the network elements. Another alternative

approach is that network operator can directly drive the network configuration parameters for the network elements

to ensure the network service provision, the parameters can be enhanced based on the current SA5 network

resource model [18].

To deliver the network provision and performance assurance for 5G vertical services, the intent model and intent

enforce scope needs to be investigated. One potential intent model for this use case is to express the intent with the

network objectives. The scope can be used to identify the network slice type, application type, QoS flow. It may

also include the time/duration and geography constraints. The network objective for the intent may vary due to

different vertical service requirements. Taking the electronic vertical service as an example, the intent objective

may include service/slice type together with the service/slice profile, e2e latency, guaranteed UL/DL flow bit rate,

synchronization timing requirement, etc.

4.2.3 Use Case 3: intent/policy driven network optimization for user QoE assurance

HD video, cloud VR and gaming are envisioned to be the most promising applications in early 5G era. These

applications are usually bandwidth consuming, or even latency sensitive. For such traffic-intensive and highly

interactive applications, with potentially significant fluctuation of radio transmission capability, users may often

suffer from stalling, which causes the user QoE deterioration. Operators have already been strive hard to guarantee

network performance for the network KPI perspectives, but the assurance of KPIs sometimes cannot effectively

reflect the actual user experience. Instead of the traditional KPI or QoS targets delivered by the network, the user

QoE is the actual crucial factor to guide the network optimization.


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QoE is normally measured at application layer based on application layer info, but it’s also possible for network to

estimate or predict the user experience based on network side info and then perform network optimization for QoE

assurance. Multi-dimensional data, e.g. user traffic data, network measurement report, application layer QoE value,

can be acquired and used for offline ML model training of a QoE estimation model. The model inference will be

executed in a real-time manner in the mobile communication system to estimate or predict the user experience

based on network side info. The estimated QoE value at network side is approximately to the QoE value which will

be measured at application layer later, but it’s estimated at network in real time, so network could decide to

optimize the network resource in real time to improve the user experience.

4.2.4 Use Case 4: Intent driven network performance optimization

Due to some user complaint information received, the operator expresses his intent that the performance of network

needs to be optimized to satisfy certain requirements. For example, the percentage of users with experienced data

rate < 5 Mbps should be less than 1 %, and the average experienced data rate should be greater than 7 Mbps. Based

on the intent received, the intent system detects the potential network issues which lead to the poor performance

(e.g. experienced data rate < 5 Mbps), which may be the handover is happened frequently between some neighbor

Cells, or some weak coverage area with large number of users, etc. An example is illustrated in Figure. 3.3.

The intent system decides the network optimization methods (e.g. coverage and capacity optimization, handover,

machine learning, etc.) to be used and derives the corresponding parameters (e.g. policy for handover) for the

selected network optimization methods. For example, intent system decides to trigger coverage and capacity

optimization to enhance the coverage for the weak areas and triggers the handover optimization to reduce the

frequency of handover between certain neighbor Cells.

The intent system may adjust and monitor the network iteratively until the performance optimization intent is

satisfied. The intent system may also notify the operator the fulfillment information of the network optimization

intent (e.g. fulfill or not)


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Figure 3.3 Intent driven network performance optimization

4.2.5 Use Case 5: intent driven traffic steering optimization

The objective of traffic steering optimization is to enhance the efficiency of resource utilization among multiple

cells/carriers as well as providing a consistent user experience. To achieve this goal, multiple radio resource

optimization algorithms can be considered, e.g., mobility load balancing, user carrier association, user flow control.

With the increasing bands, it becomes quite complicated and challenging for the network operator to steer the

traffic in a balanced distribution and provide good network performance and user experience. In this case,

intent/policy approach can simplify the problem of complex network traffic optimization. The network operator

expresses its intent/policy that the traffic in the specified scope needs to be optimized to satisfy certain optimization

objectives under given constraints (e.g., carrier priority settings for UEs with specific type of services). The traffic

optimization objectives can be cell level KPIs utility functions (e.g., maximize energy efficiency, minimize power

consumption, maximize throughput, balance load) and Per-UE/Per-service level targets (e.g., latency, bit data rate).

In order to optimize network traffic dynamically and flexibly for diversified scenarios, the optimization

intent/policy is flexibly configured to reflect the per-user or per-user group or per-service level QoS objectives and

the network resource and energy efficiency demands. The network resource usage guidance, e.g., frequency bands

usage preference can also be provided via the intent services to network based on the intent of the network manager

and data analytics derived from the historical network configuration and performance data.


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Based on the intent/policy received, the network element translate the intent/policy and take necessary actions to

enforce the policy to achieve the network traffic optimization objectives.

4.3 Reference Architecture for the intent aware wireless network

Figure 3.4 Intent Aware Network Reference Architecture

According to the aforementioned use cases, different kinds of intents will be existed in the wireless network,

including:

⚫ The intent from Communication Service Consumer (Intent-CSC) enables Communication Service

Consumer (CSC) to express what CSC would like to do for the communication service management

without knowing the details of the network and service operation.

⚫ The intent from Communication Service Provider (Intent-CSP) enables Communication Service provider

(CSP) to express an intent about what CSP would like to achieve in the network management without

having professional knowledge of network level operations.

⚫ The intent from Network Operator (Intent-NOP) enables Network Operator (NOP) to provide what NOP

would like to do for the network resource management without having professional knowledge of

network equipment level operation or virtualized infrastructure level operation.

Figure 3.4 shows the intent aware network reference architecture, where a hierarchical intent systems are

introduced in different level of management entities such as Business Support System (BSS), Network

Management System (NMS) and Equipment Management System (EMS), etc. The intent system composes of two


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main modules: Intent Engine and Intent Executor.

⚫ The Intent Engine is responsible for receiving intents and translating them to the executable actions for

network deployment and service assurance or other intents. The intent is expressed in declarative

language, i.e., only describe what to do rather than how to do. The translation depends on the

management knowledge that can be defined by experts or learned from the historical data.

⚫ The Intent Executor interacts with the underlying networks to execute the policies and the tasks derived

from the intent by the Intent Engine. The Intent Executor may utilize closed-loop automation mechanisms

to continuously monitor the network performance and service experience, analyses the abnormal event

and adjust the network automatically to meet the intent requirement. Some SON and slicing automatic

mechanisms can be reused by the intent system if needed.

The Intent aware network management and the traditional network management are expected to be co-exist for a

long time with the network evolution. The potential way to apply intent is as follows:

⚫ For the intent system in BSS, Intent Engine receives the Intent-CSC, and translates it into Intent-CSP and

network level requirements, then may invoke Intent system or the traditional management service in

NMS to fulfil the intent-CSC..

⚫ For the intent system in NMS, Intent Engine receives the Intent-CSP from the BSS and translates it into

sub-intent for NOP or network element level requirements, then may invoke Intent system or the

traditional management service in EMS to fulfil the intent-CSP.

⚫ For the intent system in EMS, Intent Engine receives the Intent-NOP from the NMS and translates it into

detailed network element operational requirements, then takes internal closed-loop automation to fulfill

the intent-NOP.

4.4 Intent Expression Model

An intent is a desire of the service consumer/service provider/network operator to reach a certain objective or state

for a network deployment or service assurance. The intent expression can be a machine-readable declarative

language, and it must be interpreted by the intent engine without any ambiguity.

The main body of the intent expression describes the main purposes that the intent is intended to achieve. It

indicates the target object of the intent (e.g., cells, base stations, VNFs, QoS flows, etc.), and the actions that need

to be perform to the objects (e.g., cell rehoming) or some desired objectives need to achieve (e.g., average UE

throughput larger than 10Mbps).The intent expression may also include the specification of the target objects (e.g.,


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the location/area information, software version, TDD or FDD, etc.), the constraints on when the intent takes

effective (e.g., the time duration, threshold of certain KPIs, etc.), and some extra information to be referenced by

the intent fulfillment process. The extra information can be the deployment scenarios of the network (e.g., dense

urban, high speed, etc.) or the service types (e.g., eMBB, URLLC, mMTC, etc.).

4.5 Technical Key Issues

⚫ Intent modeling and translation

The intent system needs to translate the intent into network requirements and corresponding management tasks and

continuously maintain the fulfillment of the intent in the network. Firstly, a suitable language model need to be

established. Then, with the language model and the network corpus in the network knowledge base, the required

network KPI and optimization objective are extracted from the intention expression using semantic processing

algorithms such as named entity recognition. Finally, according to the prior knowledge in the network knowledge

base, the corresponding management tasks (e.g., operational workflow or optimization algorithm) are derived.

⚫ Intent verification and conflict resolution

It is necessary to perform consistency check on the intent translation results and take appropriate actions or policies

to apply so as to ensure the accuracy of intent translation and fulfillment. And during the intent fulfillment process,

management actions or policies can be dynamically adjusted since the wireless network is a time-varying

environment. Moreover, the intent system needs to consider the conflict resolution with existing intents in the

network when new intents arrive since the new intents and the previously activated intents may overlap in terms of

operation scopes, network element parameters, scheduling of wireless network resources, etc. It may cause the

problems such as parameter adjustment conflicts, insufficient allocation of wireless network resources, and degrade

of network performance. After an intent conflict is detected by intent system, it will provide a resource adjustment

strategy for resolving the intent conflict, improving the network management efficiency, and avoiding the current

network performance degradation.

⚫ Intent assurance and network performance optimization

The key challenge of intent assurance is the adaptive adjustment of network parameters for time-varying wireless

network. With the collected data (e.g., UE measurement data, real-time wireless performance data) , the wireless

intent system extracts the temporal and spatial characteristics of the data, and then t evaluate whether the network


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state meet the intent requirements after executing the current configuration policies. If the intent requirements have

been met, current network configuration will be kept. Otherwise, the intent system will find the optimal

configuration to meet the intent requirement according to the current network state and historical data.

4.6 Intent Service Evolution Consideration

The wireless network is envisioned to have 5 levels of autonomy along its evolution path, from assisted operation

& maintenance (Level 1), partial autonomous network (Level 2), conditional autonomous network (Level 3), highly

autonomous network (Level 4) to full autonomous network (Level 5) [3] . The fulfillment of the intents relies on

the autonomous capabilities of the wireless network. With the network automation evolution from level 2 to level 5,

management capabilities (e.g., requirement mapping, context awareness, abnormal detection, decision and

execution) and management tasks (e.g., design, deployment, maintenance and optimization) are gradually replaced

and enhanced by the machine, which makes the intent translation and fulfillment more mature, so that the intent

expression can cover more scenarios and the intent fulfillment can be done with closed-loop automation and be

more effective and robust.

5 Standards Progress in 3GPP/ITU/ETSI and Consideration

5.1 ITU-T ML5G

In July 2019, ITU-T SG13 approved the Architectural framework for machine learning in future networks

including IMT-2020 submitted by the focus group in March 2019 as a technical specification. This specification

contains the following information:

• Terminology for ML in the context of future networks

• Architecture framework for machine learning in future networks and 5G

• Guidance for applying the architecture framework to specific technologies (such as 3GPP, MEC, edge

network, or transport network).

Now, ITU-T FG-ML5G has started the second phase (which lasts until July 2020). The following recommendations

and supplement were consented and approved by SG13 in June and Oct. 2019 meeting.

• Architectural framework for machine learning in future networks including IMT-2020 [19]


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• Use cases and basic requirements of machine learning in 5G and future networks [4]

• Data handling framework to enable Machine Learning in 5G and future Networks [20]

• Methods for evaluating the intelligence level of mobile networks.

5.2 3GPP

5.2.1 SA2

3GPP SA2 Enablers for Network Automation for 5G’s fRel-16 work finished this June 2019. It’s output has been

documented in New TS23.288, "Architecture enhancements for 5G System (5GS) to support network data analytics

services (Release 16)". In this TS, following aspects have been defined:

- the reference architecture for data analytics;

- network data analytics functional description;

- procedures for analytics exposure, for data collection,

- procedures for specific analytics for following use cases: Slice load level, Observed Service experience

related, NF load, Network Performance, UE mobility, UE communication, and Expected UE behavioral

parameters;

- NWDAF services description.

3GPP SA2 now continues this topic’s work in Rel-17, and the study target will be finished in March 2020. This

study focuses on: multi-NWDAF architecture for roaming scenario, new use cases, the interaction between

NWDAF and AI training platform, and deployment for specific scenarios.

5.2.2 SA5

3GPP SA5 Intent driven management service for mobile network (IDMS_MN) started in Sep. 2018, including two

phases: study item and work item. This work aims to study mobile network communication intent-driven

management scenarios that improve O&M efficiency and define intent-driven management service interfaces to

implement automatic closed-loop control, and to implement simplified control of complex networks or services.

The study item is expected to finish in end of 2019. The objective of it is as follows:

- Investigate the potential scenarios which could benefit to the operators from the intent driven

management services.


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- Study the scenarios which may utilize automation mechanisms including SON to satisfy the intent

driven purposes.

- Investigate the appropriate mechanism to describe the intent.

Currently (V0.7.0, Sep. 2019), the content of its document (3GPP TR 28.812) contains the concepts, 16 scenarios

and the standard consideration of intent driven management service.

The work item will start in Oct. 2019, whose objective is:

- Specify the typical management scenarios which operators could benefit from the intent driven

management services in the multiple vendor environment. The scenarios should help to improve the

multiple vendor operational efficiency on network management.

- Specify the intent driven management services’ requirements which are derived from the scenarios

above.

- Specify the intent driven management services which may include the management operations,

management entities and management information.

In September 2019，3GPP SA5 also started a new study on enhancement of Management Data Analytics Service

(MDAS), its objectives are as follows [21]:

• The use cases, potential requirements and possible solutions regarding the relation and interaction

between MDAS and other NF functionalities (including NWDAF and SON functionalities).

• Management aspects (e.g., in connection with Machine Learning techniques) of Management Data

Analytics.

• Development of scenarios (including exploration of potential consumers), use cases, potential

requirements and possible solutions (including the recommended probable actions) for the following

aspects, based on the MDAS concept:

• Root cause analysis for ongoing network and service configuration and performance related issues;

• Prevention of potential issues affecting the network and service operation, e.g., the preventative actions

are required to avoid network and service performance degradation;

• Prediction of network demands (e.g. capacity upgrades and new/re-deployments) to accommodate

potential load change and/or new service deployments;

• SLA assurance, for example related to network slices.


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• Support of the optimization of networks, services and functionalities;

• Support of the automation of network and service management;

• The management data analytics capability to support B2B services;

• The management data analytics capability exposure and data collection mechanism with the vertical

management system (e.g. AF management system);

• Input and output data of the MDAS to realize the items above-mentioned.

5.2.3 RAN3

3GPP RAN WG3 started RAN-centric Data Collection and Utilization SI in June 2018. The objective is to study

the wireless big data collection and application oriented to network automation and intelligence, including the

process and information interaction required for studying different use cases. During the study phase, the work

focuses on studying the use cases and benefits of RAN centric Data utilization, e.g. SON features including

mobility optimization, RACH optimization, load sharing/balancing related optimization, Minimization of Drive

testing (MDT) etc. Based on the evaluations on the use cases, Identify necessary standard impact and the procedure

for configuration and collection of UE measurements, L2 gNB/en-gNB measurements and signalling procedure for

problem analysis.

In June 2019,the study phase finished and the corresponding WI was approved in RAN#84 meeting which aimed

to be completed in February 2020. Detailed discussion on the impact to the spec are ongoing in RAN2 and RAN3,

including impact to NGAP.S1AP,x2AP,XnAP,E1AP,F1AP and also the Uu interface. Until now,50% is completed.

5.3 ETSI ENI

ETSI ISG ENI Intent Aware Network Autonomy (ITANA) work item started in Jun. 2019, and expected to finish

in Jul. 2020. The scope of it is: "This Work Item will describe the motivation, requirements, and key issues of using

intent policies to manage the operation of networks and networked applications in various domains. This Work

Item will explore how to define and use intent policies. Options include (1) define intent using a Controlled

Language and translate that to software, (2) define intent using a Controlled Language and translate that to a DSL,

(3) define intent using a DSL and translate that to software, or (4) use a DSL to define and use intent.

This document will discuss various design options, in terms of a set of new stand-alone and/or nested Functional

Blocks, for using intent with the ENI System Architecture. This includes accepting and validating intent statements,


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determining how intent affects the goals and operation of the ENI system, and how it is used by business users,

application developers, and network administrators. The recommendations and conclusions of this report will be

contributed to other ENI group specifications.

6 Summary

Towards the smart wireless network, this whitepaper tries to investigate the data driven and intent aware

wireless network. The use cases and potential solutions for data driven wireless network are discussed and

updated based on the recent progress and our previous two whitepapers. The ML algorithms utilized in the all the

use cases of the series of whitepaper are also discussed and summarized to provide some insight for the researcher

on the future works. Leveraging the data driven solutions, to further enhance the network intelligence, simplify the

network management complexity and offer simplified management capability towards the vertical industries, the

intent aware wireless network is proposed. The motivation, concept, use cases and reference architecture are

presented. The intent expression model, technical key issues and intent service evolution are discussed as well.

The standards progress related to wireless big data and AI as well as the of potential standards impacts are also

discussed.

As the key guiding documents for future works, the series whitepaper on smart 5G/wireless network are

expected to help readers better understand how to bring the WBD and AI/ML into wireless network towards the

intelligence wireless ear. The latest achievements will be presented in annual updates of this white paper or

technology review reports. A collective wisdom and especially more efforts among SIG members and others

partners are continuously expected.

We sincerely invite all mobile operators, telecom equipment manufacturers and IT system manufacturers, as

well as industrial and academic research institutions of interest to join us and work closely to deliver the smart

wireless network vision.

7 Reference

[1] FuTURE Forum 5G SIG, Whitepaper, “Wireless Big Data for Smart 5G”, Nov. 2017, online available:

http://www.future-forum.org/dl/171114/whitepaper2017.rar.

[2] FuTURE Forum 5G SIG, Whitepaper, “Mobile AI for Smart 5G-Empowered by Wireless Big Data”, Nov.

2017, online available: http://www.future-forum.org/dl/171114/2018mwc.rar.

[3] FuTURE Forum 5G SIG, Whitepaper, “Wireless Big Data and AI for Smart 5G & Beyond,” 2018.

[4] ITU-T Supplement 55 to Y.3170 series, “Machine learning in future networks including IMT-2020: use

cases”, 2019.


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[5] Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu. 2013. 3D convolutional neural networks for human action

recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 1 (2013), 221–231.

[6] P. V. Klaine, A. I. Muhammad, O. Oluwakayode, et al., “A Survey of Machine Learning Techniques Applied

to Self Organizing Cellular Network,”, IEEE Commun. Surveys &Tutorials, 2017

[7] Y. Hua, R. Li, Z. Zhao, H. Zhang, and X. Chen, “GAN-based Deep Distributional Reinforcement Learning for

Resource Management in Network Slicing,” in Proc. IEEE Globecom 2019, Big Island, Hawaii, USA, 201

[8] R. Li et al., “Deep reinforcement learning for resource management in network slicing,” IEEE Access, vol. 6,

pp. 74429–74441, Nov. 2018

[9] Jia Guo, Chenyang Yang and I Chih-Lin, "Exploiting Future Radio Resources with End-to-end Prediction by Deep

Learning," IEEE Access, vol. 6, pp. 75729-75747, 2018.

[10] Kaiyang Guo, Tingting Liu, Chenyang Yang, and Zixiang Xiong, "Interference Coordination and Resource

Allocation Planning with Predicted Average Channel Gains for HetNets," IEEE Access, vol. 6, pp. 60137-60151,

2018.

[11] Zhaoqi Xu, Jia Guo, and Chenyang Yang, “Predictive Resource Allocation with Interference Coordination by Deep

Learning,” IEEE WCSP 2019

[12] Jia Guo and Chenyang Yang, “Predictive Resource Allocation with Deep Learning,” IEEE VTC Fall 2018.

[13] Dong Liu, Jianyu Zhao and Chenyang Yang, “Energy-Saving Predictive Video Streaming with Deep Reinforcement

Learning,” IEEE GLOBECOM 2019.

[14] Changyang She and Chenyang Yang, “Energy Efficient Resource Allocation for Hybrid Services with Future

Channel Gains”, IEEE Trans. on Green Communications and Networking, early access.

[15] M.Huang and X.Zhang, “Big data analysis on beam spectrum for handover optimization in massive-MIMO

cellular systems,” Proc. Of 2018 IEEE WCNC, 2018.

[16] N. Ye, X. Li, H. Yu, A. Wang, W. Liu and X. Hou, “Deep Learning Aided Grant-Free NOMA Toward

Reliable Low-Latency Access in Tactile Internet of Things,” IEEE Trans. Industrial Informatics, vol. 15, no.

5, pp. 2995-3005, May 2019.

[17] 3GPP TS28.531, Technical Specification Group Services and System Aspects; Management and orchestration;

Provisioning.

[18] 3GPP TS28.541, Technical Specification Group Services and System Aspects; Management and orchestration;

5G Network Resource Model (NRM); Stage 2 and stage 3

[19] ITU-T Recommendation Y.3172 (2019), “Architectural framework for machine learning in future networks

including IMT-2020”

[20] ITU-T Recommendation Y.3174 (2019), “Framework for data handling to enable machine learning in future

networks including IMT-2020”

[21] 3GPP TSG-SA5 Meeting #126, S5-195631, Study on enhancement of Management Data Analytics Service.

Acknowledgement

Grateful thanks to the following contributors for their wonderful work on this whitepaper:

Leaders:


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China Mobile: Chih-Lin I, Chunfeng Cui, Qi Sun, Yuxuan Xie, Jie Wu, Yali Guo, Gang Li

Huawei: Yan Wang

Datang Mobile: Haichao qin, Ming Ai

ZTE: Dong Chen

Contributors:

Intel: Jianfeng Wang, Xu Zhang

DOCOMO

Beijing Labs: Wenjia Liu, , Xiaolin Hou

China Unicom: Feibi Lv, Rong Huang, Shan Liu

ZJU: Honggang Zhang, Rongpeng Li

LENOVO: Xin Guo

BUAA: Chenyang Yang, Shengqian Han, Tingting Liu

XIDIAN

University Zhenfei Xia, Liqiang Zhao

Abbreviation

3GPP the 3rd Generation Partner Project

5G the Fifth-Generation mobile communications

AI Artificial intelligence

CSC Communication Service Consumer

CSP Communication Service provider

CP Central Processor

CNN Convolutional neural network

DNN Deep Neural Network

DNNrx DNN-based receiver

DRL Deep Reinforcement Learning

DQL Deep Q learning

GNSS Global Navigation Satellite Systems

KPI Key Performance Indicator


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LSTM Long Short Term Memory

NRM Network Resource Model

PLS Partial Least Squares Regression

PRA Predictive resource allocation

PRA-ICIC PRA with ICI coordination

QoE Quality of Experience

RAN Radio Access Network

RSRP Reference Signal Receiving Power

RSRQ Reference Signal Receiving Quality

VOD Video-on-Demand

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