CRAN_white_paper_v1 14

C-RAN The Road Towards Green RAN

White Paper

Version 1.0.0 (April, 2010)

China Mobile Research Institute


i

Table of Contents

1 Introduction ............................................................................................................................ 1

1.1 Background ......................................................................................................................... 1

1.2 Vision of C-RAN .................................................................................................................. 1

1.3 Objectives of this White Paper ....................................................................................... 2

1.4 Status of this White Paper ............................................................................................... 2

2 Terms and Definitions ......................................................................................................... 3

3 Challenges of Today’s RAN ............................................................................................... 5

3.1 Large Number of BS and Associated High Power Consumption .............................. 5

3.2 Rapid Increasing CAPEX/OPEX of RAN.......................................................................... 5

3.3 Explosive Network Capacity Need with Falling ARPUs............................................... 7

3.4 Dynamic mobile network load and low BS utilization rate ....................................... 8

3.5 Growing Internet Service Pressure on Operator‟s Core Network............................ 9

4 Architecture of C-RAN ....................................................................................................... 11

4.1 Advantages of C-RAN ..................................................................................................... 12

4.2 Technical Challenges of C-RAN ..................................................................................... 13

5 Research Framework ........................................................................................................ 15

5.1 Efficient Transmission of Radio over Optical Transport Networks ........................ 15

5.2 Dynamic Radio Resource Allocation and Cooperative Transmission / Reception18

5.3 Soft Defined Radio on General Purpose Processor and BS Virtualization .......................... 20

5.4 Service on the Edge with Distributed Service Network ........................................................ 23

6 Step-by-Step Evolution Path .......................................................................................... 24

6.1 BS with Distributed RRH+BBU on Optical Transport Network ............................... 24

6.2 BS SDR Implementation with Joint Processing ......................................................... 24

6.3 Virtual BS on Real-time Cloud Infrastructure ............................................................ 24

7 Conclusions and Call for Action .................................................................................... 26

8 Acknowledgement .............................................................................................................. 27

9 Reference ............................................................................................................................... 28

Cover is for

position only


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1 Introduction

1.1 Background

Today‟s mobile operators are facing a strong competition environment. The expenses to build,

operate and upgrade the Radio Access Network (RAN) has become more and more expensive

while the revenue is not growing at the same rate. The mobile internet traffic are surging, while

the ARPU is flat or even decreasing slowly. These are hurting the mobile operators‟ profitability.

To maintain profitability and growth, mobile operators must find solutions to reduce cost as well

as to provide better services to the customers.

On the other hand, the proliferation of mobile broadband internet also presents a unique

opportunity for developing an evolved network architecture that will enable new applications

and services, and become more energy efficient.

The RAN is the most important asset for mobile operators to provide high data rate, high

quality, and 7x24 services to mobile users. Traditional RAN architecture has the following

characteristics: firstly, each BS only connects to a fixed number of sector antennas that cover a

small area and only handle transmission/reception signals in its coverage area; secondly, the

system capacity is limited by interference, and it is very hard to improve spectrum capacity;

lastly, BSs are built on proprietary platforms as a vertical solution. These characteristics have

resulted in the following challenges. The large number of BSs requires corresponding initial

investment, site support, site rental and the others. Building more BS sites means increasing

CAPEX and OPEX. Then, BS‟s utilization rate is low because the average network load is usually

far lower than that in peak load, while the BS‟ processing power can not be shared with other

BSs. Isolated BS is hard to improve spectrum capacity. Lastly, the proprietary platform means

mobile operators must manage multiple none-compatible platforms if they purchase system

from multiple vendors; and operators need more complex and costly plan for network

expansion and upgrading. To meet the fast increasing data services, mobile operators today

need to upgrade their network frequently and operate multiple-standard network, including

GSM, WCDMA/TD-SCDMA and LTE. However, the proprietary platform means mobile operators

lack the flexibility in network upgrade.

In summary, traditional RAN will become far too expensive for mobile operators to keep

competitive in the future mobile internet world. It lacks the efficiency to support sophisticated

centralized interference management required by future heterogeneous networks. It also lacks

the flexibility to migrate services to network edge for innovative applications and new revenue.

Therefore, the RAN should be re-architected to adapt to the new environment. The problem

that mobile operators are facing is to find a way to build up cost-effective Radio Access Network.

In the following, we will explore the solution for this problem.

1.2 Vision of C-RAN

The future RAN should provide mobile broadband Internet access to wireless customers with

low bit-cost, high spectral efficiency and energy efficiency. It should meet the following

requirements:

Reduced cost (CAPEX and OPEX) and lower energy consumption

High spectral efficiency

Based on open platform, support multiple standards, and smooth evolution


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Better internet services to end user

We believe the Centralized processing, Co-operative radio, and real-time Cloud infrastructure

RAN (C-RAN) is the answer. Centralized signal processing greatly reduces the number of site‟s

equipment room needed to cover the same areas; Co-operative radio with distributed antenna

equipped by Remote Radio Head (RRH) provides higher spectrum efficiency; real-time Cloud

infrastructure based on open platform and BS virtualization enables processing power

aggregation and dynamic allocation, reduces the power consumption and increases

infrastructure utilization rate. These novel technologies well solve the challenges that mobile

operators are facing today and also meet the requirements above.

C-RAN doesn‟t target to replace the current 3G/B3G air interface standard. It is an architecture

that can deploy the current and future air interfaces, to provide broadband access and wireless

services to mass mobile users from a long term perspective.

The environment varies a lot in the real world, thus the RAN deployment solution for different

scenario is also different. Today, there are macro cell station, micro cell station, pico cell station,

indoor coverage system, repeater and new emerging BS types like relay station and Femto

station. They each suit for particular deployment scenarios. C-RAN is targeting to be applicable

to most typical RAN deployment scenarios, like macro cell, micro cell, pico cell and indoor

coverage. In addition, other BS types can serve as complementary deployment for certain case.

1.3 Objectives of this White Paper

The objective of this white paper is to present China Mobile‟s vision of C-RAN and provide a

research framework by identifying the technical challenges of C-RAN architecture. We would

like to invite both industry and academic research institutes to join the research to push the

vision into reality in the near future.

1.4 Status of this White Paper

This document version 1.0 is a first sufficiently stable version to be released. It is not yet fully

complete and there may still be some inconsistencies. However, it is considered to be useful for

distribution at this stage. It is expected that new research challenges might be added in future

versions. Comments and contributions to improve the quality of this white paper are welcome.


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2 Terms and Definitions

This section provides the terms and definitions for this document.

3GPP 3rd Generation Partnership Project

AIS Alarm Indication Signal

ASIC Application Specific Integrated Circuit

ARPU Average Revenue Per User

BBU Base Band Unit

BS Base Station

CAGR Compound Annual Growth Rate

CAPEX Capital Expenditure

CBF Coordinated Beam-Forming

CDN Content Distribution Network

CoMP Cooperative Multi-point processing

C-RAN Centralized, Cooperative, Cloud RAN

CSI Channel State Information

CT/CR Cooperative Transmission/Reception

DPI Deep Packet Inspection

DSP Digital Signal Processing

DSN Distributed Service Network

eNB Evolved Node B

FEC Forward Error Correction

FFTX Fiber To The X

FPGA Field Programmable Gate Array

GGSN Gateway GPRS Support Node

GPP General Purpose Processors

GSM Global System for Mobile Communications

HW/SW Hardware/Software

ICI Inter-cell Interference

IQ In-phase/Quadrature-phase)

I/O Input/Output

JP Joint Processing

LTE Long Term Evolution

LTE-A Long Term Evolution - Advanced

MAC Media Access Control

MIMO Multiple Input Multiple Output

MNC Mobile Network Controller

OBRI Open BBU RRH Interface

OFDM Orthogonal Frequency Division Multiplexing

OPEX Operating Expenditure


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OTN Optical Transmission Net

O&M Operations and Maintenance

P2P Peer to Peer

PA Power Amplifier

PHY Physical Layer

Pon Passive Optical Network

QoS Quality of Service

RAN Radio Access Network

RF Radio Frequency

RNC Radio Network Controller

RRH Remote Radio Head

RRM Radio Resource Management

SDR Software defined Radio

SFP Small Form-factor Pluggable

SGSN Serving GPRS Supporting Node

TCO Total Cost of Ownership

TDD Time Division Dual

TD-SCDMA Time Division-Synchronous Code Division Multiple Access

TEM Telecom Equipment Manufacturer

TP Transmission Point

UE User Equipment

UL/DL Uplink/Downlink

UMTS Universal Mobile Telecommunications System

UniPon Unified Passive Optical Network

VNI Visual Networking Index

VoIP Voice over IP

WCDMA Wideband Code Division Multiple Access

WDM wavelength Division Multiplexing

XENPAK 10 Gigabit Ethernet Transceiver Package

XFP 10-Gigabit small Form-factor Pluggable


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3 Challenges of Today’s RAN

3.1 Large Number of BS and Associated High Power Consumption

As operators constantly introduce new air interface and increase the number of base stations to

offer broadband wireless services, the power consumption gets a dramatic rise. Take China

Mobile as an example: in the past 5 years, China Mobile has almost doubled its number of BS,

to provide better network coverage and capacity. As a result, the total power consumption has

also doubled. The higher power consumption is translated directly to the higher OPEX and a

significant environmental impact, both of which are now increasingly unacceptable.

The following figure 1 shows the components of the power consumption of an example operator.

It shows the majority of power consumption is from BS in the radio access network. Inside the

BS, the only half of the power is used by the major equipment; while the other half is

consumed by air condition and other facilitate equipments.

Obviously, the best way to save energy and decrease carbon-dioxide emissions is to decrease

the number of BS. However, for traditional RAN, this will result in worse network coverage and

lower capacity. Today, there are quite a number of „amendment‟ technologies that helps reduce

BS‟ power consumption, such as and turning off selected carriers on idle hours like midnight,

adapting reproducible energy sources like solar and wind power, reducing the requirement for

air- conditioning, etc. However, these technologies are not addressing the fundamental

problems of power consumption with the number of increasing BS.

In the long run, mobile operators must plan for energy efficiency from the radio access network

architecture planning, like applying centralized BS to reduce the number of BS equipment

rooms, reduce the A/C need, and use resource sharing mechanisms to improve the BS

utilization rate efficiency under dynamic network load. The change in infrastructure is the key

to resolve the power consumption challenge of radio access network.

Fig.1 Power Consumption of Base Station

3.2 Rapid Increasing CAPEX/OPEX of RAN

Over recent years, mobile data consumption has experienced a record growth among the

world‟s operators as subscribers start to use smart phones and laptop cards. Therefore, mobile

operators must significantly increase their network capacity to provide mobile broadband to the

masses. However, in an intensifying competitive marketplace coupled with an economic

downturn, high saturation levels, rapid technological changes and falling voice Average

Revenue Per User (ARPU) are all affecting mobile operators‟ profitability. They become more

and more cautious about the Total Cost of Ownership (TCO) of their network.


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Fig 2: Increasing CAPEX of 3G Network Construction and Evolution

Analysis of the TCO

The TCO including the CAPEX and the OPEX results from the network construction and

operation. The CAPEX is mainly associated with network infrastructure build, while OPEX is

mainly associated with network operation and management.

In general, up to 80% CAPEX of a mobile operator is spent on the RAN. This means that most

of the CAPEX is related to building up cell sites for the RAN. The historical CAPEX expenditure of

2007-2009 and the next three-year forest are shown in Fig.2. Because 3G/B3G signals have

higher path loss and penetration loss than 2G signals, more cell sites are needed for the similar

level of 2G coverage. We can find the dramatic increase in the CAPEX when building a 3G

network.

The CAPEX is mainly spent at the stage of cell site constructions and consists of purchase and

construction expenditures. Purchase expenditures include the purchases of BS and

supplementary equipments, such as power and air conditioning equipments etc. Construction

expenditures include network planning, site acquisition, civil works and so on. As shown is Fig.3,

it is noticeable that the cost of major wireless equipments makes up only 35% of CAPEX, while

the cost of the site acquisition, civil works, and equipment installation is more than 50% of the

total cost. Essentially, this means that more than half of CAPEX is not spent on productive

wireless functionality. Therefore, ways to reduce the cost of the supplementary equipment and

the expenditure on site installation and deployment is important to lower the CAPEX of mobile

operators.

Fig 3: CAPEX and OPEX Analysis of Cell Site

OPEX in network operation and the maintenance stage play a significant part in the TCO.

Operational expenditure includes the expense of site rental, transmission network rental,

operation /maintenance and bills from the power supplier. Given a 7-year depreciation period of

BS equipment, as shown in Fig.4, an analysis of the TCO shows that OPEX accounts for over

60% of the TCO, while the CAPEX only accounts for about 40% of the TCO. The OPEX is a key

factor that must be considered by operators in building the future RAN.


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The most effective way to reduce TCO is to decrease the number of sites. This will bring down

the cost for the construction of the major equipment; and will minimize the expenditure on the

installation and rental of the equipment incurred by their occupied space. Fewer sites means

the corresponding cost of supplementary equipment will also be saved. This can significantly

decrease the operators‟ CAPEX and OPEX, but results in poorer network coverage and user

experience in the traditional RAN. Therefore, a more cost-effective way must be found to

minimize the non-productive part of the TCO while simultaneously maintaining good network

coverage.

Fig 4: TCO Analysis of Cell Site

Multi-standard environment

It is taken that the large number of legacy terminals, 2G, 3G, and B3G infrastructure will

coexist for a very long time to meet consumers‟ demand. Most of the major mobile operators

worldwide will thus have to use two or three networks (Table 1) [1]. In the new economic

climate, operators must find ways to control CAPEX and OPEX when growing their businesses.

The base station occupies the largest part of infrastructure investment in a mobile network.

Multi-mode base station can be expected as a cost efficient way for operators to alleviate the

cost of network construction and O&M.

Table 1. Multi-Network Operation of Major Mobile Service Providers

Cellular Technologies Vodafone China

Mobile

France

Telecom

T-

Mobile

Verizon SK

Telecom

Telstra China

Unicom

TD-SCDMA √

WCDMA √ √ √ √ √ √

CDMA One & 2000 &

EVDO √ √

GSM GPRS EDGE √ √ √ √ √ √

LTE √ √ √ √

3.3 Explosive Network Capacity Need with Falling ARPUs

Data rate of mobile broadband network grows greatly with the introduction of air-interface

standards such as 3G and B3G; this in turn speeds up end user‟s mobile data consumption.

Some forecasts indicated the number of people who access mobile broadband will triple in next

several years, after LTE and LTE-A are deployed. These findings also reflect the fact that the

increasing bandwidth of wireless broadband triggers the increase in mobile traffic, because the

mobile users can use a variety of high-bandwidth services, such as video-based applications.


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Based on the forecast data [2], global mobile traffic increases 66-fold with a compound annual

growth rate (CAGR) of 131% between 2008 and 2013, the similar trend is observed in current

CMCC network. On the contrary, the peak data rate from UMTS to LTE-A only increases with a

CAGR of 55%. Clearly, as shown in Fig.5, there is a large gap between the CAGR of new air

interface and the CAGR of customer‟s need. In order to fill this gap, new infrastructure

technologies should be developed to further improve the performance of LTE/LTE-A.

Fig 5: Mobile Broadband Data-rates/Traffic Growth

On the other hand, the revenue of mobile operators is not increasing at the same pace as the

bandwidth they provide. Mobile operators‟ voice volumes are steadily increasing and the data

volume grows quickly, but revenues are not and ARPUs are even falling in some case. In order

to face the slow growth in revenue, operators should constantly hold down costs – notably

operating costs. That means mobile operators must find a low cost, high-capacity access

network with novel techniques to meet the growth of mobile data traffic while keeping a

healthy, profitable growth.

3.4 Dynamic mobile network load and low BS utilization rate

One characteristic of the mobile network is that subscribers are frequently moving from one

place to another. From data based on real operation network, we noticed that the movement of

subscribers shows a very strong time-geometry pattern. Around the beginning of working time,

a large number of subscribers move from residential areas to central office areas for work;

when the work hour ends, subscribers move back to their homes. Consequently, the network

load moves in the mobile network with a similar pattern. As shown in Fig.6, during working

hours, the core office area‟s Base Stations are the busiest; in the non-work hours, the

residential or entertainment area‟s Base Stations are the busiest.


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Fig 6: Mobile Network Load in Daytime

The problem is that, each Base Station‟s processing capability today can only be used by the

active users in its cell range. When subscribers are moving to other areas, the Base Station just

stays in idle with a large of its processing power wasted. Because operators must provide 7x24

coverage, these idle Base Stations consume almost the same level of energy as they do in busy

hours. Even worse, the Base Stations are often dimensioned to be able to handle the maximum

number of active subscribers in busy hours, thus they are designed to have much more

capacity than the average needed, which means that most of the processing capacity is wasted

in non-busy time.

To solve this problem, the traditional RAN architecture must be changed, so that the processing

power of each individual Base Station can be shared among cells of different areas.

3.5 Growing Internet Service Pressure on Operator’s Core Network

With the hyper-growth of smart phones as well as emerging 3G embedded Internet Notebook,

the mobile internet traffic has been grown exponentially in the last few years and will continue

to grow more than 66x in the next 5-6 years. However because of increasingly competition

between mobile operators, the projected revenue growth will be much lower than the traffic

growth. There will be a huge gap between the cost associated with this mobile internet traffic

and the revenue generated, let alone the mobile operators needing to spend billions of dollars

to upgrade their back-haul and core network to keep up with the growing pace. This is a huge

common challenge to all the mobile operators in the wireless industry.

The exponential growth of mobile broadband data puts pressure on operators‟ existing packet

core elements such as SGSNs and GGSNs, increasing mobile Internet delivery cost and

challenging the flat-rate data service models. The majority of this traffic is either Internet

bound or sourced from the Internet. Catering to this exponential growth in mobile Internet

traffic by using traditional 3G deployment models, the older 3G platform is resulting in huge

CAPEX and OPEX cost while adding little benefit to the ARPU. Key issues are the continuous

CAPEX spending on older SGSNs & GGSNs, the higher Internet distribution cost, the congestion

on backhaul and the congestion on limited shared capacity of base stations. Therefore,

offloading the Internet traffic, as close to the base stations as possible, can be an effective way

to reduce the mobile Internet delivery cost.

Fig 7: Wireless traffic on a commercial 3G

Meanwhile it is interesting to understand how people are using today‟s mobile internet. A recent

research paper [3] published by one major TEM may give us a glimpse of the most popular


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mobile applications. It is surprising to see that people are gradually using mobile internet just

like they use the fixed broadband network. Content services which include content delivered

through web and P2P are actually dominating the network traffic. Fig.7 is an example of

wireless traffic on a commercial 3G operator. Considering this usage pattern, do we have better

choice than just blindly spending billions of dollars to upgrade back-haul and the core network?


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4 Architecture of C-RAN

We believe Centralized processing, Cooperative radio, and Cloud infrastructure Radio Access

Network (C-RAN) is the answer to solve all the challenges mentioned above. As shown in Fig.8,

C-RAN is mainly composed of three parts: the distributed radio network equipped by Remote

Radio Heads (RRHs) and antennas, high bandwidth and low-latency optical transport network

which connects RRHs and BBU pool, as well as centralized baseband processing (BBU) pool

composed of high-performance general purpose processors and real-time virtualization

technology.

Distributed RRHs provide a radio access network in a wide area to ensure coverage and high

capacity. Because the RRHs are small and easy to be installed, their CAPEX and OPEX will be

much lower, and can be deployed in a large area with relatively higher density. On the other

hand, all RRHs must be connected to baseband units (BBUs) through the bandwidth-efficient

and low-latency optical transport network.

The baseband processing pool is built using high-performance general purpose processors; it

can aggregate the processing power through real-time virtualization technology and provides

the required signal processing capacity to the virtual BS in the pool. The centralized BBU pool

largely reduces the number of BS rooms needed, and makes it possible for resource

aggregation and large-scale cooperative radio transmission/reception.

Fig 8: C-RAN Architecture

In C-RAN, mobile operators can rapidly deploy their networks and easily scale up their network.

The operator only needs to install new RRHs and connect them to the centralized baseband pool

to expand network coverage or split the cell to improve capacity. If the network load grows, the

operator only needs to upgrade the BBU pool‟s HW to accommodate more general purpose

processors. Moreover, open platform and general purpose processors provide an easy way to

develop and deploy software defined radio (SDR), which enables updates of air interface

standards through software only, and makes it easier to upgrade RAN and support multi-

standard operation.


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Different from traditional distributed BS architecture, C-RAN breaks up the static relationship

between RRHs and BBUs. Each RRH does not belong to any specific physical BBU. The radio

signals from /to a particular RRH can be processed by a virtual BS, which is part of the

processing power allocated from one physical BBU pool by the real-time virtualization

technology. The adoption of virtualization technology will allow the physical resources to be

used in an optimal way.

4.1 Advantages of C-RAN

The benefits of the C-RAN architecture are listed as follows:

Energy Efficient

C-RAN is an eco-friendly infrastructure. First, With the C-RAN architecture, the number

of BS sites can be reduced several folds. Thus the air conditioning and other site

support equipments‟ power consumption can be largely reduced. Secondly, because the

BBU pool is a shared resource among a large number of virtual BS, it means a much

high utilization rate of processing resources and a lower power consumption. When a

virtual BS is idle at night and most of the processing power is not needed, they can be

selectively turned off while not affecting the 7x24 service commitment. Lastly, the

distance from RRHs to UEs can be decreased since the cooperative radio technology can

reduce the interference among RRHs and have a higher density of RRHs. Smaller cells

with lower transmitting power can be deployed while the network coverage quality is

not affected. The energy used for signal transmitting will be reduced, which is especially

helpful to maintain the UE battery level and results in reduction of power consumption

in the RAN.

Cost-saving on CAPEX &OPEX

Because the BBUs and site support equipments are aggregated in a few big rooms, it is

much easier for centralized management and operation, saving a lot of O&M cost

associated with the large number of BS sites in a traditional RAN network. Secondly,

although the number of RRHs may not be reduced in C-RAN architecture its

functionality is simple, size and power consumption are both small and they can sit on

poles with minimum site support and management. RRH only requires the installation

of the auxiliary antenna feeder systems, enabling operators to speed up the network

construction to gain a first-mover advantage. Thus, operators can get large cost saving

on site rental and O&M.

Capacity Improvement

In C-RAN, virtual BS‟s can work together in a large physical BBU pool and they can

easily share the signaling, traffic data and channel state information (CSI) of active

UE‟s in the system. It is much easier to implement joint processing & scheduling to

mitigate inter-cell interference (ICI) and improve spectral efficiency. For example,

cooperative multi-point processing technology (CoMP in LTE-Advanced), can easily be

implemented under the C-RAN infrastructure.

Adaptability to Non-uniform Traffic

C3-RAN is also suitable for non-uniformly distributed traffic due to the load-balancing

capability in the distributed BBU pool. Though the serving RRH changes dynamically

according to the movement of UEs, the serving BBU is still in the same BBU pool. As

the coverage of a BBU pool is larger than the traditional BS, non-uniformly distributed


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traffic generated from UEs can be distributed in a virtual BS which sits in the same BBU

pool.

Smart Internet Traffic Offload

Through enabling the smart breakout technology in C-RAN, the growing internet traffic

from smart phones and other portable devices, can be offloaded from the core network

of operators. The benefits are as follows: reducing back-haul traffic and cost; reducing

core network traffic and gateway upgrade cost; reducing latency to the users;

differentiating service delivery quality for various applications. The service overlapping

the core network also supplies a better experience to users.

4.2 Technical Challenges of C-RAN

The centralized C-RAN brings lots of benefits in cost, capacity and flexibility over traditional

RAN, however, it also has some technical challenges that must be solved before deployment by

mobile operators.

Radio over Optical Network

The optical fiber between BBUs (Remote Radio Unit) and RRHs (Base Band Unit) has to

carry a large amount of baseband sampling data in real time. Due to the wideband

requirement of LTE/LTE-A system and multi-antenna technology, the capacity of optical

transport link to transmit multiple RRHs‟ baseband sampling data is at multiple gigabit

level.

Cooperative Transmission/Reception

Joint processing is the key to achieve higher system spectrum efficiency. To mitigate

interference of the cellular system, multi-point processing algorithms that can make use

of special channel information and realize the cooperation among multiple antennas at

different physical sites should be developed. Joint scheduling of radio resources is also

necessary to reduce interference and increase capacity.

To support the above Cooperative Multi-Point Joint processing algorithms, both end-

user data and UL/DL channel information needs to be shared among virtual BSs. The

interface between virtual BSs to carry this information should support high bandwidth

and low latency to ensure real time cooperative processing. The information exchanged

in this interface includes one or more of the following types: end-user data package, UE

channel feedback information, and virtual BS‟s scheduling information. Therefore, the

design of this interface must meet the real-time joint processing requirement with low

backhaul transportation delay and overhead.

Base Station Virtualization Technology

After the processing units have been put in a centralized pool, it is essential to design

virtualization technologies to distribute/group the processing units into virtual BS

entities. The major challenges of virtualization are: real-time processing algorithm

implementation, virtualization of the baseband processing pool, and dynamic

processing capacity allocation to deal with the dynamic cell load in system.

Service on Edge

Unlike service in a data center, distributing services on the edge of the RAN has its

unique challenges. In the following research framework part, we try to summarize

these challenges into following three categories: services on the edge‟s integration with

the RAN, intelligence of DSN, and the deployment and management of distributed

service.


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5 Research Framework

5.1 Efficient Transmission of Radio over Optical Transport

Networks

Requirement analysis of OBRI link for LTE and LTE-A

The C-RAN architecture, which consists of the RRH and BBU, is an attractive solution for 3G

and beyond wireless systems. The base-band processing unit and the RF part of BS are

separated and interconnected with OBRI (Open BBU-RRH Interface) interface. Along with

the evolution of 3GPP TD-LTE to LTE-Advanced, the multi-hop connection as well as the

high-order MIMO configuration up to 8 antennas will impose the challenging requirements

on the OBRI link. Because of that, the ability to implement a low-cost, bandwidth-efficient

and low-latency optical transport network has become a major challenge of C-RAN.

In general, the system bandwidth, the MIMO antenna configuration and the concatenation

levels are the main factors which have an impact on the OBRI bandwidth requirement. For

example, the bandwidth for 20MHz LTE systems with 8Tx/8Rx antennas is up to

9.8304Gbps. At the evolution phase of LTE-Advanced, this bandwidth requirement will

sharply expand to 49.152Gbps. Moreover, if considering multi-hop connection, the

transmission load on the OBRI interface will increase proportionally with concatenation

levels. An alternative solution is to bundle several low-rate optical modules together to

increase the transmission bandwidth. For instance, 10G transmission rate can be matched

by bundling four 2.5Gbps optical fibers, which may reduce the overall implementation cost.

For different transmission rates, there are various commercial optical modules available

currently with ranges from 0.55km to over 80km. Fig. 9 outlines the lowest price history of

commercial optical modules about 2.5G SFP and 10G SFP/XFP/XENPAK. It is shown that the

minimum cost has decreased about 50%~70% in the last 2 years. This remarkable price

drop trend should continue over the next few years, which will further reduces the cost of

the optical transport network.

Fig 9: Price history of Commercial 2.5G/10G Optical Modules

Some other baseline requirements on the OBRI link are also very strict. Round trip time is

defined as the downlink delay plus the uplink delay, and the absolute round trip time for U-

↓60%

↓61.5%

↓35.2%

0

500

1000

1500

2000

2500

3000

Aug-07 Feb-08 Aug-08 Feb-09 Aug-09

Pri

ce

his

tory

of

2.5

G m

od

ule

s (R

MB

).

10Km 40Km 80Km

↓66.7%

↓54.2%

↓62.2%

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Aug-07 Feb-08 Aug-08 Feb-09 Aug-09

Pri

ce h

isto

ry o

f 1

0G

mo

du

les

(RM

B).

550m 10Km 40Km


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plane data (IQ data) on the interface, excluding the round trip delay on the transmission

medium (i.e. excluding the cable length), shall not exceed the maximum value 5μs. The

OBRI interface shall enable periodic measurement of the cable length of each link or each

multi-hop connection. Concerning the delay calibration, the accuracy of the measurement

of round trip group delay of one link or multi-hop connection shall meet the requirement of

±16.276ns [4].

Data Compression Techniques of OBRI

Several data compression techniques that can reduce the burthen on the OBRI interface

have been investigated to deal with the inevitable bandwidth issue, including time domain

schemes, e.g. reducing signal sampling, non-linear quantization, and IQ data compression,

as well as frequency domain schemes, e.g. sub-carrier compression. In order to make

clearly understandable, the feasibility of various OBRI transmission schemes, the pros and

cons are summarized in the following table (Table 2).

Table 2. Comparison of Pros and Cons for Various Data Compression Techniques

Bandwidth

Compression

Schemes

Pros Cons

Reducing signal

sampling

Low complexity;

Efficient compression to 66.7%;

Less impacts on protocols.

Severe performance loss.

Non-linear

quantization

Improve the QSNR;

Mature algorithms available, e.g. A law

and U law;

High compression efficiency to 53%.

Some impacts on the OBRI interface

complexity.

IQ data

Compression

Potential high compression efficiency;

Only need extra decompression and

compression modules.

High complexity;

Difficult to set up a relativity model;

Real-time and compression distortion

issues;

No mature algorithm available.

Sub-carrier

Compression

High compression efficiency to 40%

~58%;

Easy to be performed in downlink.

Increase the system complexity;

Extra processing ability on optical chips

and the thermal design;

High device cost;

Difficulty for maintenance;

RACH processing is a big challenge; More

storage, larger FPGA processing

capacity.

OBRI on Optical Transport Network

Operators may use different deployment strategies to satisfy the high bandwidth needed by

OBRI. The current WAN based on optical transport network is mainly composed of three

layer of ring architecture: the core transport layer, the convergence transport layer and the

access transport layer. All the layers are using ring topology to provide fail safe protection.

The optical resources of different layers are similar to the following: at the core transport

layer, each optical route has 144 to 576 fibers; at the convergence transport layer, each

route has 96-144 fibers; while at the access transport layer, each route has 24-48 fibers.

According to the resource of WAN optical transport network, especially the fiber resource in

the access transport network, there are three baseband pool OBRI transport solutions

possible: 1. Dark fiber; 2. WDM on fiber; 3. Unified Fixed and Mobile access on UniPON.


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These three solutions have both benefits and shortcomings and are each suitable for

different cases.

The first solution is suitable when there is plenty of fiber resource. The OBRI is directly

transported via dark fiber to connect the RRH and BBU. The benefits of this solution are:

fast deployment; low cost because no additional optical transport network equipment is

needed. The concern of this solution is it may consume too much fiber resource, and it

lacks protection mechanisms and it is hard to do O&M, reducing the reliability of the WAN.

Although it may be cheap initially, when additional fiber resources are needed, it may

require new investment on both new channel constructions and optical cable installations.

The second solution is suitable for cases when there is limited fiber resource. By upgrading

the optical access transport network to WDM, the bandwidth of optical network is largely

improved. The problem is the cost. However, because the access transport network is

usually within a few tens of kilometers, its WDM equipment can be much cheaper than

those used in long backhaul optical transport networks.

The third solution is based on CWDM technology. It can serve the fixed broadband and

mobile access network at the same time, thus named as „Unified PON‟. It can provide both

FTTx and 3G/B3G OBRI transport at low cost. The production is under development [5]. In

the UniPON standard, the uplink and downlink channel are transmitted on difference

wavelengths. Because UniPON is usually for new deployments in dense cities, it is suitable

to carry the OBRI for RRH of in-door coverage system.

Technical Challenges

From above analysis, existent data compression schemes can reduce the OBRI bandwidth

requirement to 50%~60% of the original data. However, this efficiency is still not sufficient

to solve the OBRI transmission issue thoroughly at the LTE-A phase. A possible hypothesis

is to combine several bandwidth compression schemes together, which will be left behind

for now as an open issue and to be further studied. In particular, the implementation of

OBRI over UniPon based on the WDM should be considered as an attractive solution.

Today some mobile operators have plenty of fiber resources in their WAN optical transport

network. How to maximum the use of these resources, or upgrade them to satisfy the

needs of the OBRI interface of C-RAN still needs further study. UniPON technology based

on WDM is a promising solution for certain deployment scenarios and it must be designed

to be competitive in cost.


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5.2 Dynamic Radio Resource Allocation and Cooperative

Transmission / Reception

One key target for C-RAN system is to significantly increase average spectrum efficiency and

the cell edge user throughput efficiency. However, users at the cell boundary are known to

experience large inter-cell interference (ICI) in a fully-loaded OFDM cellular environment, which

will cause severe degradation of system performance and can not be mitigated by increasing

the transmit power of desired signals. To improve system spectrum efficiency, advanced multi-

cell joint RRM and cooperative multi-point transmission schemes should be adopted in the C-

RAN system.

Cooperative Radio Resource Management for multi-cells

The multi-cell RRM problem has been addressed in various academic studies using the

optimization techniques, trying to determine the optimal resource scheduling and the power

control solutions to maximize the total throughput of all cells with some specific constraints. To

reduce the complexity incurred in the C-RAN network architecture and the scheduling process,

the joint processing/scheduling should be limited to a number of cells within a “cluster”. The

complexity of scheduling among the eNBs clusters is determined by the velocity of mobile users

and the number of UEs and RRHs in the cluster. Thus, choosing an optimal clustering approach

is weighing the balance among the performance gain, the requirement of backhaul capacity and

the complexity of scheduling.

As shown in Fig.10, UEs will be served by one of the available cluster which is formed in a static

or semi-static way based on the feedback or measurements reports of UEs. In this scenario, a

subset of cells within a cluster will cooperate in transmission to the UEs associated with the

cluster. To further reduce the complexity, it is possible to limit the number of cells cooperating

in joint transmission to a UE at each scheduling instant. The cells in actual transmission to a UE

are called active cells for the UE. The active cells can be defined from the UE perspective based

on the signal strength (normally cells with strong signal strength are chosen among cells within

the supercell). The activation/de-activation of a cell can be done by a super eNB, which is the

control entity in cell clustering and can adjust the sets scope based on the UE feedback.

Cell cluster 1Cell cluster 2

Cell cluster 3

Fig.10 : The UE assisted network controlled cell clustering

Cooperative Transmission / Reception

Cooperative transmission / reception (CT/CR) is well accepted as a promising technique to

increase cell average spectrum efficiency and cell-edge user spectrum efficiency. Although

CT/CR naturally increases system complexity, it has potentially significant performance benefits,


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making it worth a more detailed consideration. To be specific, the cooperative transmission /

reception is characterized into two classes, as shown in Fig.11:

Joint processing/transmission (JP)

The JP scheme incurs a large system overhead: UE data distribution and joint

processing across multiple transmission points (TPs); and channel state information

(CSI) is required for all the TP-UE pairs.

Coordinated scheduling and/or beam-forming (CBF)

With a “minimum” cooperation overhead, to improve the cell edge-user throughput via

coordinated beam-forming: No need for UE data sharing across multiple TPs; Each TP

only needs CSI between itself and the involved UEs (no need for CSI between other

TPs and UEs).

Fig. 11 JP scheme and CBF scheme

In this section, the performance of the JP scheme and the CBF scheme are evaluated in a TDD

system. We assume that full DL channel state information (CSI) can be obtained ideally at the

eNB side. The downlink throughput and spectrum efficiency results with different schemes in an

8 antenna configuration are shown in Fig.12. Detailed simulation parameters can be found in

[6-9].

Fig. 12 Downlink throughput and spectrum efficiency simulation results

Compared with non-cooperation scheme (codebook based precoding), the proposed CBF

scheme and JP scheme can obtain 68% gain and 115% gain in average cell spectrum efficiency

respectively, and 51% gain and 83% gain in cell-edge user spectrum efficiency respectively.


There are many topics that should be studied in further research, including:

Advanced joint processing schemes


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DL channel state information (CSI) feedback mechanism

User pairing and joint scheduling algorithms for multi-cells

Coordinated Radio resource allocation and power allocation schemes for multi-cells.

5.3 Soft Defined Radio on General Purpose Processor and BS

Virtualization

Centralized Baseband Pool

There are already many distributed BS solutions with an RRH+BBU architecture. Some TEM‟s

equipment has realized dynamic allocation of carrier processing within one BBU to adapt to

dynamic workloads among different RRH. This architecture can be viewed as the first step of

centralized baseband pool concept, yet not able to support dynamic resource allocation across

different BBU, thus hard to resolve the dynamic network load in larger area.

In the current RRH+BBU architecture, the RRH is usually fixed linked to a particular BBU. This

makes it difficult for another BBU to get the baseband signal to/from another BBU. Because of

this limitation, the processing resource of different BBU can hardly be shared: the idle BBU‟s

processing resource is just wasted and it can not be used to help the BBU with a heavy

workload.

The centralized baseband pool should provide a high bandwidth, low latency switch matrix with

appropriate protocol to support the inter-connection between multiple BBUs. For a middle size

TD-SCDMA baseband pool that covers a 25 km2 area, it has roughly 300~600 carriers/sectors.

This means the switch matrix needs to support 100Gbps-200Gbps full rate capacity. For TD-LTE

system with 100 carriers/sectors, that means the switch matrix needs to support

250Gbps~1Tbps capacity. With this switch capacity, the centralized BBU could realize carrier

processing load balance among BBUs. This will improve the overall BBU utilization rate, adapt

the dynamic network load and reduce power consumption. It also makes the deployment of

multi-point MIMO technology and interference mitigation algorithm easier, which can improve

wireless system capacity.

Current Multi-standard Solution

Nowadays, most of the major mobile operators worldwide have to support multiple networks

covering the same area. Multi-mode base stations can be expected as a cost efficient way for

operators to control CAPEX and OPEX. According to various deployment scenarios, multi-mode

base stations can be explained in different ways.

Single BBU system supports multiple modes at the same time: The processing boards for

different standards will be put together in a single BBU system. Operators can use a single

set of BBU system to support multiple mobile networks. In this case, some modules in BBU

system, such as the control and clocking modules, and the I/O modules for RRHs can be

shared among the BBU processing boards for different standards.

Each base station hardware module can support different modes through software update:

through software upgrade or configuration, the components in base stations, such as the

processing for PHY, MAC or network interfaces, etc., can support different standards (e.g.

LTE or TD-SCDMA). In some of the latest products, the RRH can also be SDR-enabled to

support different standards if they are allocated in the same spectrum area. So, the entire

base station can be upgraded to different standard modes without any hardware

replacement.


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Evolution of General Purpose Processor

General purpose processors have traditionally been considered for application and control type

workloads. Their evolution and applicability for specialized signal and packet processing

workloads have been rapidly increasing over the last 5 years or so and many of these

workloads are now capable of efficient execution on GPP. Technology evolution in areas such

as multi-core, SIMD (single-instruction multiple data) with IEEE-754 compliant floating point

support, large on-chip caches, low latency off-chip system memory and relatively shorter

overall cadence for architectural improvements compared to DSPs are facilitating the use of

GPP in traditional signal processing applications such as baseband processing in base

stations. Figure 13 shows compute performance evolution in GFLOPs of an industry leading

GPP over a period of 6-7 years and the corresponding reduction in power consumption per

GFLOP. Some of the specific GPP architectural features that have significantly evolved &

provide benefit for base station applications are:

Multi-core scaling and hardware multi-threading to take advantage of thread level

parallelism (TLP)

Wider SIMD widths (e.g. 256-bit) and multiple ALUs with SIMD processing capability to take

advantage of data level parallelism (DLP)

Execution of multiple instructions simultaneously, to take advantage of instruction level

parallelism (ILP)

Instruction Set Enhancements for data transfer, conversion, comparison, packing, etc

Hardware based CPU and IO virtualization

Applications that require real time processing capabilities have been deployed on GPP

processors for a number of years, in variety of application categories. There is a broad

ecosystem of operating system vendors (OSV) that offer real time operating system (RTOS) on

GPPs that can form basis for real time application development such as base stations. In

addition, there are existing proof points of complete LTE baseband processing (20 MHz, 2x2

MIMO) on small number of GPP cores and still meet stringent timing requirements of 1-msec

TTI [10]. As such, GPPs now provide high compute performance with power efficiency, that

coupled with multi-core scaling and industry standard architectural features such as

virtualization, can be an good platform for implementing base stations with multi-mode

functionality.

Fig. 13: Compute performance evolution of GPP *


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(CPUs in 50-65 watt power envelopes used as basis for comparison in graph)

Base Station Virtualization

The current BS‟ BBU processing board is usually designed for a dedicated standard (such as

GSM, TD-SCDMA or LTE) and only supports a fixed number of carriers. The computation

resources (e.g. DSP, FPGA or GPP etc.) are statically assigned to the corresponding carriers‟

PHY layer or MAC layer processing.

Under the rapid evolution of wireless technology and standards, such “fixed” design will bring

following problems. 1) The same hardware platform is hard to support different standards, if

the computation load of the new standard can not be fitted into the existing components on the

hardware boards; 2) The required computation resources on PHY or MAC layers will vary along

with the load of traffic, the number of subscribers, or the configuration of air interface, etc..

Under the “fixed” design, the computation resources can not be reallocated. Thus the hardware

efficiency will be low; 3) In the novel algorithms like collaborative MIMO, the dynamic virtual

MIMO group will require the dynamic cooperative processing in the PHY layer among base

stations. It will be difficult under current “fixed” design.

Virtualization is a term that refers to the abstraction of computer resources. It hides the

physical characteristics of computing platform from users, instead showing another abstract

computing platform. If such a concept can be utilized in base station systems, those problems

brought by the “fixed” design can be removed. So, it will be expected that, in future mobile

networks, there could be a baseband pool based on BS virtualization technology as shown in

Fig. 14. The processing resources provided by physical hardware can be classified into four

categories according to their characteristics, including PHY layer resource, MAC layer resource,

accelerator resource and C&M resource. Under the virtualization technology, for a given

standard profile, the resource requirement on those four processing resource pools can be

determined. So, a base station instance can be easily built up through the flexible resource

combination.

Fig. 14: Baseband Pool

When the load of a base station instance varies, the system can determine if it will adjust the

assigned resources or not. If yes, all the adjustments will be done by software only. With this

mechanism, the base stations of different standards can be easily built up through resource

reconfigure in software. Also, cooperative MIMO can get the required processing resources

dynamically. In addition, the processing resources can be assigned in a global view, thus the

resource utilization can be improved significantly.


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Based on the real-time and high performance requirement of BS, the traditional virtualization

technology is difficult to handle this application of signal processing. In order to design a novel

virtualization technology to construct the baseband pool, the following challenges should be

well studied.

High-performance low-power GPP for real-time signal processing for wireless signals.

High efficiency, and flexible virtualization environment to provide abstraction of the

processing resources to virtual BS; it also must guarantee real time scheduling and strict

controlled processing delay and jitter.

The inter-connection topology among physical processing resources in the baseband pool.

It includes the interconnection among the chips on a processing board, among the boards

in a physical rack, and among multiple racks.

5.4 Service on the Edge with Distributed Service Network

One promising solution to the challenge of rising internet traffic on the mobile operator‟s

network is the Distributed Service Network (DSN) at the edge of the RAN [11]. As a matter of

fact, this is not the first time the IT industry is facing this kind of problem. Content Distribution

Network (CDN) has been widely used to cache content on the edge of the Internet to reduce

traffic and latency. Besides content cache, which is able to reduce unnecessary duplicate

content downloading from Internet, another important thing operators should consider is how

to squeeze the value of each “back-haul bit”. It basically means that instead of serving each

data bit equally, higher priority is given to those more valuable bits, such as those bits for

video conference, enterprise applications etc.

To enable service ot the edge of the C-RAN, the following questions need to be answered:

Integration with C-RAN

Today, there is no standard way to integrate mobile internet services with the RAN. In addition,

where to enable service on the edge is highly dependent on the economics of the RAN and

back-haul network. With the introduction of C-RAN in mobile infrastructure, the architecture of

the RAN will be largely changed. What is the best way to integrate DSN capability with C-RAN

and what is the cost model behind different solutions are still open questions.

Intelligence of DSN

The value of DSN is highly dependent on the intelligence of DSN, not only for content caching,

P2P based voice call or Diff-Serve of various services. Because each DSN node only has limited

resources, it can only selectively serve partial service requests. How to identify target

applications, and those requests within these target applications to be served at the edge will

require quite much intelligence. So DPI like capability will be an indispensable part of DSN node.

Deploy and Management Cost

Deploying and managing these distributed service resources will be another big challenge. The

Virtualization and cloud computing model could be leveraged here to reduce deployment and

management costs. The deployment and management cost will directly affect the usefulness of

DSN, because if the cost of deploying and managing a large scale DSN exceeds the benefit it

will create, there will be less motivation for operators to consider it. New distributed system

management technologies are needed to effectively manage these physically distributed service

resources in a very low cost way.


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6 Step-by-Step Evolution Path

The novel C-RAN architecture is a revolution of the traditional RAN. It is not possible to replace

today‟s RAN over night. In addition, the technical challenges of C-RAN must be carefully

developed and trialed in labs and field environments to ensure its reliability. This naturally leads

to a step-by-step evolution path of C-RAN to gradually replace traditional RANs. The following is

our vision on how this step-by-step evolution could take place:

6.1 BS with Distributed RRH+BBU on Optical Transport Network

For the first step, Base Stations can be implemented by separate Remote Radio Heads (RRH)

and Baseband Units (BBU), and gradually implement the carrier processing power allocation

among multiple BBUs. The RRHs are connected to the BBU via fiber links or optical transport

networks. RRHs can be deployed in remote sites far from the physical location of the BBU (e.g.

1~10km). The RRHs are small and light weight for easier deployment. They receive/transmit

radio signals (in either analogy or digital format) from/to the BBU. The BBU is the core of the

signal processing. The fiber link between RRHs and the BBU can be standardized like OBRI so

that RRHs and BBUs from multiple venders can be linked together.

The centralized BS should have a high bandwidth, low latency switch matrix and corresponding

protocol to support the inter-connection among multiple BBU. The signals from distributed RRH

can be forwarded to any BBU inside the centralized BS. Thus, the centralized BS can realize

dynamic allocation of carrier processing power allocation and load balance. It will reduce the

number of BS rooms and reduce the A/C need, while improving the BS utilization rate under

dynamic network load. In addition, it facilitates the implementation of cooperative

transmission/reception of radio signal, thus controls the interference among cells and improves

the spectrum efficiency.

6.2 BS SDR Implementation with Joint Processing

In the next step, the BS‟ baseband processing can be fully implemented by Soft Defined Radio

(SDR) on general purpose processor (GPP). This is different from the traditional BS

implementation method where the baseband processing is usually implemented by ASIC, FPGA

and/or DSP. Although FPGA/DSP provides some level of flexibility, they are not general purpose

processors. They don‟t promise to be backward compatible, and the HW/SW design of

FPGA/DSP is usually dedicated for a certain hardware architecture. By moving the baseband

processing to SDR on general purpose processors, it is much easier to support multiple

standards, or upgrade the SW/HW to support new standard or increase processing capacity.

With multiple RRHs attached to the BBU and SDR implementation in the BBU, it is easier to

implement coordinated beamforming (CBF) and cooperative multipoint processing (CoMP) in

the BBU. Multiple BS can coordinate with each other to share the scheduling information,

channel status and user data to improve the capacity as well as reducing interference in system.

6.3 Virtual BS on Real-time Cloud Infrastructure

Once the BBU is built on general purpose processors and the baseband processing is

implemented by software, the real-time cloud infrastructure is the next step of C-RAN evolution.

A large number of general purpose processors are connected by a high bandwidth, low latency

network, and organized into a big computing pool call „real time baseband pool‟, just like the

cloud computing environment in IT industry. The difference is that the baseband processing


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tasks are real-time computing tasks in a real time baseband pool. The computing resources in

the real time baseband pool are dynamically allocated to Virtual BSs to process wireless signals

to/from RRHs. An Optical transport network and load balancing switch connects the RRHs and

the backend real time baseband pool. The load-balancing switch should be able to forward the

signals from RRHs to designated target virtual BS in the baseband pool, in order that the

computing load is balanced across different baseband pool and meets the processing delay

requirement.


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7 Conclusions and Call for Action

Today‟s RAN architecture is facing more and more challenges that the mobile operators need to

solve: costly to build and operate, hard to improve spectrum efficiency, lack of flexibility to

multi-standard and dynamic network load and expensive to provide ever increasing internet

service to end users. Mobile operators must consider the evolution of the RAN to a high efficient

and lost cost architecture.

C-RAN is a promising solution to the challenges mentioned above. With the distributed RRH and

centralized BBU architecture, advanced multipoint transmission/reception technology, SDR with

multi-standard support, virtualization technology on general purpose processor, and service on

the edge of the RAN, C-RAN will be able to provide today‟s mobile operator with a competitive

infrastructure to keep profitable growth in the dynamic market environment.

We‟d like to invite all the mobile operators, the telecom equipment vendors, the traditional IT

system vendors, and industry/academic research institutes who are concerned on the future

evolution of the RAN to devote their intelligence and resources in the research of C-RAN to

make it a reality.


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8 Acknowledgement

We would like to thank IBM China Research Lab and Intel Cooperation for their valuable contribution to this white paper.


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9 Reference

[1] Co-Platform Multi-Mode BTS (C-P MMBTS): Leading the Trend of Multi-Mode Network Convergence, white paper from In-Stat, 2009.Multi standard

[2] Cisco Visual Networking Index, URL: www.cisco.com/web/go/vni

[3] Geza Szabo,Daniel Orincsay,Balazs, Peter Gero,Sandor Gyori,Tamas Borsos, “Traffic Analysis of Mobile Broadband Networks”, Third Annual International Wireless Internet Conference October 22-24, 2007, Austin, Texas, USA

[4] CPRI Specification V4.1, Common Public Radio Interface (CPRI); Interface Specification. 2009-02-18

[5] F.-Joachim Westphal. Trends and evolution of transport networks. SL SI, IBU Telco, SSC ENPS

[6] 3GPP, R1-093273, SRS feedback mechanism based CoMP schemes in TD-LTE-Advanced

[7] Q. H. Spencer, A. L. Swindlehurst and M.Haardt, “Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels,” IEEE Transactions on Signal Processing, vol. 52, pp. 461 – 471, Feb. 2004.

[8] L. U. Choi and R. D. Murch, “A transmit preprocessing technique for multiuser mimo systems using a decomposition approach,” IEEE Trans. Wireless Commun., vol. 3, no. 1, pp. 20–24, Jan. 2004.

[9] Jun Zhang, Runhua Chen, J. G. Andrews and R. W. Heath, “Coordinated multi-cell MIMO

systems with cellular block diagonalization,” Proc.41st Asilomar Conference on Signals,

Systems and Computers (ACSSC‟ 07), pp. 1669 – 1673, Nov. 2007.

[10] Rajesh Gadiyar, John Mangan, “Using Intel Architecture for implementing SDR in Wireless Basesations”, SDRForum, SDR09‟.

[11] White Paper of Distributed Service Network. China Mobile Research Institute.

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