Cognitive Radio

Occupancy Measurement

Mauro Vieira de Lima

Telecommunications Metrology Division

INMETRO

Duque de Caxias, Brazil

mvieira@inmetro.gov.br

Abstract — A simulation tool was developed to

performance of cognitive radio systems based on the spectral

efficiency achieved. The tool utilizes measurements of spectrum

occupancy by primary users as input data. A description of

measurements setup and methodology is presented,

consolidated results of spectral occupancy in frequency bands from

144 MHz to 2690 MHz at one site in Rio de Janeiro, Brazil.

Simulation results indicate that cognitive radio efficiency is

strongly dependent on occupancy fragmentation

scheme to improve performance of cognitive radio systems in

fragmented frequency bands is proposed.

Keywords-component; Spectral Occupancy; Spectrum

Management; Dynamic Spectrum Access; Cognitive Radio.

I. INTRODUCTION

The spectrum management by regulatory bodies is based by granting license to a fixed allocation of frequencies to a single user or to a few coordinated operators in large geographical areas. Spectral occupancy measurements show that this form of the spectrum management is inefficient, as operators subtilize a large part of the spectrum [1].

Due to the rapid proliferation of wireless communication systems and the growth of data traffic from such as smartphones, there is a huge demand for radio spectrum [2]. However, the current regulatory in a lack of spectrum needed to the forecasted capacity growth of wireless broadband systems.

The cognitive radio technology could reduce the problem of radio spectrum scarcity by the use of dynamic spectrum access(DSA) which allows unlicensed (secondary) devices to identify underutilized portion of licensed spectrum and use opportunistic way without causing significantthe licensed (primary) users. In this context, spectral occupancy measurements are needed to enable new spectrum management policies by regulatory bodies and the development of intelligent techniques for dynamic spectrum access by cognitive radios.

This paper presents a simulation tool developed to evaluate the performance of cognitive radio systems based on the spectral efficiency achieved. The tool utilizes measurements of spectrum occupancy by primary users as input data.

Cognitive Radio Simulation Based on

Measurements at One Site in Brazil

Telecommunications Metrology Division

Luiz da Silva Mello

Center for Telecommunication Studies

CETUC/PUC

Rio de Janeiro, Brazil

smello@cetuc.puc

simulation tool was developed to evaluate the

performance of cognitive radio systems based on the spectral

tool utilizes measurements of spectrum

A description of the

and methodology is presented, as well as the

in frequency bands from

one site in Rio de Janeiro, Brazil.

itive radio efficiency is

fragmentation. An allocation

cognitive radio systems in

Spectral Occupancy; Spectrum

Cognitive Radio.

regulatory bodies is mostly by granting license to a fixed allocation of frequencies to

operators in large tral occupancy measurements show

of the spectrum management is inherently a large part of the spectrum

Due to the rapid proliferation of wireless communication rom mobile devices

such as smartphones, there is a huge demand for radio regulatory scenario results

a lack of spectrum needed to the forecasted capacity growth

reduce the problem of dynamic spectrum access

which allows unlicensed (secondary) devices to identify underutilized portion of licensed spectrum and use it in an

significant interference to the licensed (primary) users. In this context, spectral occupancy

enable new spectrum management policies by regulatory bodies and the development of

amic spectrum access by

a simulation tool developed to evaluate the performance of cognitive radio systems based on the spectral efficiency achieved. The tool utilizes measurements of spectrum occupancy by primary users as input data. Section II

describes the measurements setup and methodologmetrics used to characterize spectrum occupancy and the consolidated measurements results. simulation tool and an assessmentquality. Section IV develops a study of spectrum oSection V presents the conclusions.

II. MEASUREMENTS SETUP

A. Measurements Setup

The measurement campaign of the National Metrology, Quality and Technology Institute(INMETRO) laboratorial campus in Duque de CaxiasJaneiro, Brazil, during the months of March, April and May 2012.

Measurements sweep the frequencies bands from 144 MHz to 2690 MHz for periods of 24 hoursfollowed the frequency allocation plan from telecommunications regulatory agency

The measurement system was assembled as shown in Figure 1. A laptop performs analyzer and data recording. The measurements are controlledand data transferred remotely, via Internet.

Figure 1. Measurement

Based on Spectrum

at One Site in Brazil

Luiz da Silva Mello

for Telecommunication Studies

CETUC/PUC-Rio

Rio de Janeiro, Brazil

smello@cetuc.puc-rio.br

the measurements setup and methodology, the metrics used to characterize spectrum occupancy and the consolidated measurements results. Section III describes the

assessment of the frequency bands a study of spectrum opportunities.

Section V presents the conclusions.

ETUP, METHODOLOGY AND RESULTS

The measurement campaign was performed on the grounds he National Metrology, Quality and Technology Institute

campus in Duque de Caxias, Rio de months of March, April and May

sweep the frequencies bands from 144 MHz periods of 24 hours. The band segmentation

allocation plan from the Brazilian regulatory agency - ANATEL [3].

system was assembled as shown in Figure 1. A laptop performs the control of the spectrum

. The measurements are controlled via Internet.

Measurement setup.

978-1-4799-1397-8/13/$31.00 ©2013 IEEE

B. Methodology

The measurements use a statistical approach to evaluate the spectral occupancy. Measurements duration depends on the accuracy desired for the statistical evaluation of occupancy

The smaller the occupancy of a frequency channel, larger the number of samples required for a desired confidence level. Figure 2 shows the number of samples needed a relative accuracy of 10% with a confidence level of

Figure.2. Number of samples x spectrum occupancy

(relative accuracy 10% - confidence level 95%)

In order to obtain an adequate number of samples for an occupancy level of less than 5%, the frequency span waslimited to a maximum of 100 MHz for a 10KHz RBWthat, we obtain revisit times of less than 4 seconds and the number of samples of a particular channel larger than 1

The spectral occupancy can be defined asa power measurement to be above a predefined threshold.threshold power was established as the measureplus a margin to consider the variations instantaneous noise power due to signal propagation.The margin must be reduce the probability of detection error. It should be sufficient to reduce the probability of false alarmoverestimation of the occupancy. On the other handmargin cannot be too large as it could cause misdetection and, consequently, a sub-estimation of the occupancy. Thus, aoff must be established in the selection of Following [5], a false alarm rate of about 1% margin of 6 dB above the noise floor.

C. Metrics for Occupancy Analysis

The basic metric that describes the spectrum known as the duty cycle. It is the fraction of time that a frequency band is defined as busy and represents the average time of spectrum use.

Let nS be the number of samples where Ppower level threshold and PR the reception sample. It can be assumed that the time that a frequency channel was busy during the measurement period t is sweep channel duration. Thus, taking T as the total period of measurement and N the total number of samples, we can compute the duty cycle (DC) for each frequency channel as:

. ..

a statistical approach to evaluate the duration depends on the

accuracy desired for the statistical evaluation of occupancy.

occupancy of a frequency channel, the the number of samples required for a desired confidence

the number of samples needed to provide a confidence level of 95% [4].

spectrum occupancy

confidence level 95%)

In order to obtain an adequate number of samples for an the frequency span was

limited to a maximum of 100 MHz for a 10KHz RBW.With less than 4 seconds and the

articular channel larger than 10.000.

The spectral occupancy can be defined as the probability of be above a predefined threshold.The

measured noise floor the variations instantaneous noise

power due to signal propagation.The margin must be chosen to r. It should be sufficient

reduce the probability of false alarm, avoiding an On the other hand, this

margin cannot be too large as it could cause misdetection and, estimation of the occupancy. Thus, a trade-

be established in the selection of the threshold. 1% corresponds to a

spectrum occupancy is duty cycle. It is the fraction of time that a

frequency band is defined as busy and represents the average

be the number of samples where PR>PL, PL being the power level of a

the time that a frequency ng the measurement period is nS.t, where

taking T as the total period and N the total number of samples, we can

) for each frequency channel as:

(2.1)

Note that duty cycle providesthe frequency channel was busy, but notevents occur. Aiming to analyze temporal patterns of occupation, plots of channel usewaterfall graphs, can be used. Fcycle and waterfall graphs.

Figure.3. Duty cycle and waterfall

D. Consolidated Results of Spectral Occupa

A cognitive radio device performs sensingspectrum opportunities, known as Minimizing time and energy sensing allows increasing transfer and giving more autonomy to the device batteryCognitive radios must decide which bands and channels sense and can use prior knowledgeanalysis of spectral occupancy [

The average occupancies of each fin our measurements, are consolidated in figure 4, allowing compare and visualize the occupation of the measured. The underutilization of the spectrum is evidenthe graph. An average occupancy of only 19.6% of themeasured bands was observedbands have occupancies larger mobile services and one for TV subscription service (MMDS). The results show that mobile servicesefficiency of spectrum use.

Figure.4. Measured a

(a)

provides the percentage of time that y channel was busy, but not when such occupation

to analyze temporal patterns of of channel use versus time, known as

Figure 3 shows examples of duty

aterfall graphs for the 806-902 MHz band.

Consolidated Results of Spectral Occupancy

A cognitive radio device performs sensing searches for opportunities, known as white spaces (WS). time and energy sensing allows increasing the data

transfer and giving more autonomy to the device battery. ognitive radios must decide which bands and channels to

prior knowledge obtained from the statistical [6].

of each frequency band, obtained consolidated in figure 4, allowing

the occupation of the whole spectrum measured. The underutilization of the spectrum is evident in

average occupancy of only 19.6% of the all measured bands was observed. Only 7 of the 32 frequency

larger than 30%, six of then used by mobile services and one for TV subscription service (MMDS).

mobile services have the highest

average spectral occupancy.

(b)

III. QUALITY ASSESSMENT OF FREQUENCY

COGNITIVE RADIO USE

The consolidated spectral occupancy shown allows to comparison and identification frequency bands less occupied for opportunistic use. In this section, an assessment of the frequency bands quality of a DSA cognitive radio scheme.

The tool developed uses occupancy data measurements to simulate primary users.This approach allows associating the performance of the simulated system quality of the frequency range for use in a cognitive radio system.

A. Cognitive Radio System Simulation

The use of measured data simulates the behavior Users (PU) that is an advantage over PU artificial generation [7] [8] [9]. Secondary Users (SU) are generated Poisson distribution for the arrival rate. The premises of the simulation are described below.

(a) The system is centralized, i.e., the cognitive manages channel allocation of SU.

(b) The system uses channel aggregationrequired bandwidth.

(c) When a SU wants to communicate, the procedure is as follows: (i) the SU requests a channel to the radiobase(ii) the radiobase performs a channel algorithm and informs the channel to (iii) the SU initiates a communicationallocated channel.

(d) When a PU begins its communicationchannel in use by an SU, the SU stopreleasing the channel for PU and returning

Figure 5 shows the simulation tool block diagram

Figure.5. Simulation tool block diagram

The simulation was performed a hundred timesthe following performance parameters: (i) Throughputchannels per second) indicates how many times the radiobase

REQUENCY BANDS FOR

shown in section II frequency bands less

In this section, it will be shown quality using simulation

data from the spectrum This approach allows

associating the performance of the simulated system to the in a cognitive radio

behavior of Primary PU artificial generation

generated assuming a . The premises of the

the cognitive radiobase

aggregation to provide the

the procedure is as

radiobase; channel allocation

to SU; a communication using the

communication through a stops its transmission, returning to step (c).

ion tool block diagram.

lock diagram

hundred times to generate (i) Throughput (in

how many times the radiobase

meets the SU requests; (ii) Forced Termination many times a SU in service is appeared inside its channel; (iii) times a SU arrives in the system and there is not enough channels to serve it. Figure parameters as a function of the arrival ratenormalized in relation to the maximum observed value

Figure.6.Simulation performance parameters

B. Spectral Efficiency Assessment

The spectral efficiency per channel from simulated cognitive radio system was calculated. Spectral efficiency is the rate that can be transmitted through a system bandwidth, measured in bits/s/Hz. To calculate throughput inadopted, as a reference, the spectral efficiency standard "release" 8 to 1.4 MHz channel, resulting efficiency of 1.74 bit/s/Hz [10].

Figure 7 shows the occupancy by primary usersand the spectral efficiency of secondary users asIn general, the higher the occupancy, the lower the spectral efficiency of the cognitive radio system and vice

Figure.7.Occupancy

Note that there are bands with similar occupations, such as 19302110 MHz bands. The last of these bands higher spectral efficiency compared to others.

Studying this difference by channel waterfall graphs of Figure the 1930-1970 MHz band of Figure fragmentation, characterized by the scattering of the occupation, while in 2025-2110 MHz band of Figure occupation is less fragmented.

Forced Termination indicates how many times a SU in service is turned off because a PU

(iii) Blocking indicates how many rives in the system and there is not enough

Figure 6 shows the performance as a function of the arrival rate. The values are

normalized in relation to the maximum observed value.

Simulation performance parameters.

ssessment

The spectral efficiency per channel from simulated cognitive radio system was calculated. Spectral efficiency is the rate that can be transmitted through a system bandwidth,

To calculate throughput in bit/s, it was adopted, as a reference, the spectral efficiency from LTE

to 1.4 MHz channel, resulting in a spectral .

ncy by primary users as wide bars of secondary users as narrow bars.

the occupancy, the lower the spectral cognitive radio system and vice-versa.

and efficiency per channel.

Note that there are bands with different efficiencies and , such as 1930-1970, 1970-2025 and 2025-

2110 MHz bands. The last of these bands presents significantly higher spectral efficiency compared to others.

Studying this difference by comparing the occupancy waterfall graphs of Figure 8, it can be observed that in

band of Figure 8.a there is severe fragmentation, characterized by the scattering of the

2110 MHz band of Figure 8.b, the

Figure.8.Waterfall (a) 1930-1970 MHz (b) 2025

It appears that fragmentation is a factor that contributes to lower spectral efficiency. Thus, an assessment of frequency bands for a cognitive radio system should not be based on spectral occupancy only, but also on fragmentation.

IV. STUDY OF SPECTRUM OPPORTUNITIES

The quality assessment of the frequency bands, using the simulation tool to find out spectral efficiencyimportance of considering fragmentation in the choice of bands. Fragmentation can be understood as an intrinsic characteristic of the primary service in the However, some factors can worsen the fragmentation

The channeling increases the fragmfrequency band to a cognitive radio systemthat is partially occupied will be considered busy throughout its width.Variable width of the cognitive radio channel canused to avoid performance reduction. To evaluate how variable width impacts in performance of a fragmented frequency band that will be used by a cognitive radio system, a twodimensional model of spectral opportunities (developed, that allows it to be used as a channel allocation scheme and evaluate system performance.

A. Two-dimensional model of white space

A White Space (WS) is characterizedfrequency and its duration in time. The first featurethe throughput of the system. The second feature isfor minimize spectral handoff. Considering that changes its width and duration according to the occupancy changes occurring in the frequency range, it to group adjacent WS to form a contiguous rectangular block of larger width and duration. To develop the WS group functions, manipulating functions of binary imageBinary images are black and white images zeros and ones, respectively. It is not be interaggregate small blocks, since the cost/benefit would be high and not provide large bandwidth to the system. Therefore, blocks with little width were discarded and only blocks greater than 1 MHz were considered.

To perform the statistical characterization frequency range, counting and classification of performed, providing the joint probability frequency width (w) and time duration (τ).

(a)

1970 MHz (b) 2025-2110 MHz

It appears that fragmentation is a factor that contributes to lower spectral efficiency. Thus, an assessment of frequency

cognitive radio system should not be based on fragmentation.

PPORTUNITIES

assessment of the frequency bands, using the simulation tool to find out spectral efficiency, highlighted the

fragmentation in the choice of Fragmentation can be understood as an intrinsic

primary service in the frequency band. However, some factors can worsen the fragmentation.

mentation of the system, since a channel

considered busy throughout its of the cognitive radio channel can be

used to avoid performance reduction. To evaluate how variable of a fragmented frequency band

used by a cognitive radio system, a two-(white spaces) was

allows it to be used as a channel allocation

is characterized by its width in The first feature is crucial to

The second feature is important Considering that the WS

its width and duration according to the occupancy it will be necessary

form a contiguous rectangular block To develop the WS group

functions, manipulating functions of binary image were used. Binary images are black and white images depicting only

not be interesting to aggregate small blocks, since the cost/benefit would be high and not provide large bandwidth to the system. Therefore,

only blocks greater

rization of WS into each classification of the WS was

the joint probability function of

Taking n() as the number of elements, and S() as the number of blockexpress as:

, ΤAnd

,

Figure 9 shows a bivariate graph of WS Density

from measurements in 806-902 MHz.

Figure.9. WS density

B. WS allocation scheme

Using the two-dimensional model and technique, a WS allocation scheme simulation tool. In this scheme, the premises remain unchanged. However, the occupancy changed take advantage of the use of WS and duration, instead of fixed channel

The WS allocation scheme can be illustrated by Figure There are 3 WS detected in the last sensing. From these current WS, 3 time windows are defined of WS that occurred in the past according to sensing history.The search for WS is performed inside each time window and the mean and standard deviation of and Duration are calculated. The criteria for the current WS are:

• lower standard deviation

• lower standard deviation for Start and

• highest mean for DurationThe criterion aims to increase throughput and reduce the

probability of collision. Therefore, seekWidths and Starts and longer Duration of WS in sensing history to get the lower chanceslargest Width is not always selected. This more important to perform a complete cgetting a larger Width at the cost of havinginterruption by a collision.

(b)

Taking n() as the number of elements, Ω as sample space blocks, the joint probability can be

, Ω

, Τ

a bivariate graph of WS Density obtained 902 MHz.

WS density (806-902 MHz)

dimensional model and the WS search technique, a WS allocation scheme was developed using the simulation tool. In this scheme, the premises about the system remain unchanged. However, the occupancy by the SU is

the use of WS with varying width , instead of fixed channel width.

The WS allocation scheme can be illustrated by Figure 10. the last sensing. From these current

defined and used to calculate statistics occurred in the past according to sensing history.

The search for WS is performed inside each time window and mean and standard deviation of the parameters Start, Width

The criteria for selecting one of

lower standard deviation and higher mean for Width;

lower standard deviation for Start and

highest mean for Duration The criterion aims to increase throughput and reduce the

herefore, seeks to obtain similar Widths and Starts and longer Duration of WS in sensing

chances of collision. As a result, the not always selected. This happens because it is

more important to perform a complete communication than at the cost of having high probability of

Figure.10.WS allocation scheme

After selecting the WS, the next step is allocation parameters to be used in SU communication. The parameters are defined by the average of Width. The Duration is not used as a parameter, because the system adopts a fixed service time.

C. Performance Comparison

A new version of the simulation tool was developed to perform the Bi-dim WS allocation scheme. The simulation was performed in the frequency bands of 1930-1970 and 2025MHz that, as presented in section III, have similar occupations and a large difference in spectral efficiency.

The Bi-dim simulation results were compared to of a fixed channel allocation scheme contiguous channels of 1MHz. The number of channels (K) used in this simulation is associated with the systemperformance, as an increase the number of channelsan increase on forced terminations.

Figure 11 shows graphs comparing the plotted with dashed lines, and the fixed channelscheme, plotted with solid lines. Red and magenta lines are from 1930-1970 MHz band, while blue and cyan lines are from 2025-2110 MHz band.

Figure.11.Allocation schemes comparison

Note that in the graphics for the band 2025Bi-dim scheme becomes better than the fixed Therefore, a bandwidth of about 3 MHz is the threshold between schemes in this frequency band. For the 1930MHz band, the Bi-dim scheme presents better throughput than

WS allocation scheme

After selecting the WS, the next step is to define the allocation parameters to be used in SU communication. The

the Start and the Width. The Duration is not used as a parameter, because the

sion of the simulation tool was developed to WS allocation scheme. The simulation was

1970 and 2025-2110 similar occupations

results were compared to the results that aggregates

The number of channels (K) simulation is associated with the system

increase the number of channels results in

graphs comparing the Bi-dim scheme, channel allocation

Red and magenta lines are 1970 MHz band, while blue and cyan lines are from

s comparison

for the band 2025-2110 MHz, the the fixed scheme for K>3.

, a bandwidth of about 3 MHz is the threshold between schemes in this frequency band. For the 1930-1970

dim scheme presents better throughput than

the fixed scheme for any value of K used.1930-1970 MHz band is more fragmenteddemonstrated that the Bi-dim scheme shows superior performance for any value of K in fragmented bands, while in bands less fragmented the performance is superior above a certain threshold value K.

V. CONC

This paper presented a comparative evaluation of the frequency bands in terms of its quality for use by an opportunistic cognitive system. This assessment was possible by the use of measurements of spectral occupancyprimary users in a simulation tool of a cognitive radio system. In this way, the system performancefrequency band quality.

The evaluation leads to the conclusion thatoccupation, fragmentation is an important factor for the quality of frequency bands for cognitive radiomodel and an allocation scheme performance in fragmented bands.better performance in fragmented frequency bands with systems based on variable width using

As future work, a field test to evaluate the performance of a cognitive radio system in fragmented frequencyperformed.

ACKNOWLEDGMENT

This work was supported by INMETRO and the CETUC/PUC-Rio.

REFERENCES

[1] Alexander M., Wyglinski, Maziar Nekovee, and Y. Thomas Hou; “Cognitive radio communications and networks”; Elsevier, 2010.

[2] “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2012–2017”, Cisco,at:http://newsroom.cisco.com/documents/10157/1142732/Cisco_VNI_Mobile_Data_Traffic_Forecast_2012_2017_white_paper.pdf

[3] “Plano de Atribuição, Destinação e Distribuição de Faixas de Frequências Anatel 2011”, Anatel, 2011

[4] Recommendation ITU-R SM.1880, "Spectrum occupancy measurement", ITU-R, 2011.

[5] Robin I. C. Chiang; Gerard B. Rowe and Kevin W. SowerbyQuantitative Analysis of Spectral Occupancy Measurements for Cognitive Radio”, 1550-2252, IEEE

[6] Miguel López-Benítez and Fernando CasadevallOccupancy Perception of Cognitive Radio Terminals in Realistic Scenarios”, 2nd International Workshop on Cognitive InfProcessing, 2010.

[7] Furong Huangy , Wei Wangy z , Haiyan Luoy , Guanding Yuy , Zhaoyang Zhangy, “PredictionHardware Limitation in Cognitive Radio Networks8/10, IEEE, 2010.

[8] Jongheon Lee and Jaewoo So, "Analysis of Cognitive Radio Networks with Channel Aggregation",978-1

[9] T. M. N. Ngatched, Attahiru S. Alfa, and Jun Cai, "Analysis of Cognitive Radio Networks with Channel Aggregation and Imperfect Sensing", 978-1-4577-0638-7 /11, IEEE, 2011.

[10] H. Holma and A. Toskala,“LTE for UMTS: OFDMA and SCBased Radio Access”, John Wiley & Sons, 2009.

for any value of K used. Reminding that the 1970 MHz band is more fragmented. The results

dim scheme shows superior performance for any value of K in fragmented bands, while in bands less fragmented the performance is superior above a

ONCLUSIONS

This paper presented a comparative evaluation of the frequency bands in terms of its quality for use by an

system. This assessment was possible surements of spectral occupancy to represent simulation tool of a cognitive radio system.

performance can be associated to the

to the conclusion that, besides the fragmentation is an important factor for assessing

s for cognitive radio. A new WS allocation scheme were developed to improve

fragmented bands. The results indicate that fragmented frequency bands are obtained variable width using statistics of WS.

s future work, a field test to evaluate the performance of a fragmented frequency bands will be

CKNOWLEDGMENT

This work was supported by INMETRO and the

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Alexander M., Wyglinski, Maziar Nekovee, and Y. Thomas Hou; “Cognitive radio communications and networks”; Elsevier, 2010.

Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Cisco,February, 2013. Available

http://newsroom.cisco.com/documents/10157/1142732/Cisco_VNI_Mobile_Data_Traffic_Forecast_2012_2017_white_paper.pdf

Atribuição, Destinação e Distribuição de Faixas de Frequências Anatel 2011”, Anatel, 2011.

R SM.1880, "Spectrum occupancy

Gerard B. Rowe and Kevin W. Sowerby,“A ectral Occupancy Measurements for

IEEE, 2007.

Benítez and Fernando Casadevall, “On the Spectrum Occupancy Perception of Cognitive Radio Terminals in Realistic

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Hardware Limitation in Cognitive Radio Networks”, 978-1-4244-2519-

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H. Holma and A. Toskala,“LTE for UMTS: OFDMA and SC-FDMA Based Radio Access”, John Wiley & Sons, 2009.