UNIVERSITA’ DEGLI STUDI DI PAVIA FACOLTA’ DI INGEGNERIA 

Dottorato di Ricerca in Ingegneria Elettronica, Informatica ed Elettrica XXIII Ciclo 

  

       

 

Orthogonal Frequency Division Multiplexing Systems and Application in Cognitive Radio Networks 

              

Ph.D. Thesis of Anna Vizziello 

 Advisor                 Prof. Lorenzo Favalli                   

 Academic Year 2009/2010 

Contents

Introduction 1

Part I: OFDM Systems and Inter Carrier Interference Mitigati on 6

1 Orthogonal Frequency Division Multiplexing Systems 7

1.1 Wireless Propagation . . . . . . . . . . . . . . . . . . . . . . . 7

1.2 Principles of OFDM . . . . . . . . . . . . . . . . . . . . . . . 13

1.2.1 Orthogonality . . . . . . . . . . . . . . . . . . . . . . . 14

1.2.2 Cyclic Prefix, Inter-Symbol and Inter-Carrier Interference 16

2 Mitigation of Intercarrier Interference in OFDM Systems 19

2.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1.1 Transmission model . . . . . . . . . . . . . . . . . . . 22

2.1.2 Frequency Channel Matrix approximation . . . . . . . . 23

2.2 Proposed algorithm for ICI Mitigation . . . . . . . . . . . . . . 24

2.2.1 Generalized EM algorithm for joint ICI estimation and

equalization . . . . . . . . . . . . . . . . . . . . . . . . 25

2.2.2 Implementation of the Proposed Algorithm . . . . . . . 26

2.2.3 Simplified Algorithm . . . . . . . . . . . . . . . . . . . 29

2.3 Simulation results and comments . . . . . . . . . . . . . . . . . 29

Part II: Application of OFDM to Cognitive Radio Networks 36

I

3 Cognitive Radio Networks 37

3.1 Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.1.1 Physical Architecture of the Cognitive Radio . . . . . . 42

3.1.2 Cognitive Radio Network Architecture . . . . . . . . . 44

3.1.3 Spectrum Management Functions . . . . . . . . . . . . 48

3.2 Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.2.1 PU Activity Models . . . . . . . . . . . . . . . . . . . 52

3.2.2 PU Detection . . . . . . . . . . . . . . . . . . . . . . . 53

3.3 Spectrum Decision . . . . . . . . . . . . . . . . . . . . . . . . 60

3.4 Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.4.1 Overview of Spectrum Sharing Techniques . . . . . . . 62

3.4.2 Overview of MAC Protocols . . . . . . . . . . . . . . . 63

3.5 Spectrum Mobility . . . . . . . . . . . . . . . . . . . . . . . . 65

3.6 Routing Layer in Cognitive Radio Networks . . . . . . . . . . . 67

3.6.1 Factors influencing routing protocols design . . . . . . .68

3.6.2 Overview of the classification of routing protocols . .. 69

3.7 Transport Layer in Cognitive Radio Networks . . . . . . . . . . 70

3.8 Standards for Cognitive Radio Networks . . . . . . . . . . . . . 71

4 OFDM Signals Recognition in Cognitive Radio Networks 73

4.1 Motivation for OFDM Signals Recognition . . . . . . . . . . . 74

4.2 System Architecture and Modules . . . . . . . . . . . . . . . . 76

4.3 Primary Users Type Characterization . . . . . . . . . . . . . . . 78

4.3.1 Primary Users Activity Model . . . . . . . . . . . . . . 78

4.3.2 Cyclostationary Feature Detector/Classifier . . . . . . . 79

4.3.3 PU Characteristics Module . . . . . . . . . . . . . . . . 82

4.4 Adaptability Effects on Cognitive Radio . . . . . . . . . . . . . 84

4.4.1 CR Adaptive Parameters . . . . . . . . . . . . . . . . . 84

4.4.2 CR Throughput/Interference Adapter . . . . . . . . . . 86

4.5 Performance evaluation and comments . . . . . . . . . . . . . . 89

II

4.5.1 Simulation Environment . . . . . . . . . . . . . . . . . 89

4.5.2 CAF Detector/Classifier . . . . . . . . . . . . . . . . . 89

4.5.3 CR Adaptive Throughput . . . . . . . . . . . . . . . . . 91

4.5.4 Main comments . . . . . . . . . . . . . . . . . . . . . . 93

5 Radio Resource Management in Cognitive Radio Networks 94

5.1 Proposed System Architecture . . . . . . . . . . . . . . . . . . 97

5.1.1 PU Type Features Extraction . . . . . . . . . . . . . . . 98

5.1.2 Available Capacity Calculation . . . . . . . . . . . . . . 100

5.2 Optimization Framework . . . . . . . . . . . . . . . . . . . . . 101

5.3 Proposed Suboptimal Solution . . . . . . . . . . . . . . . . . . 105

5.3.1 Resource Allocation inside a single cluster . . . . . . . 108

5.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . 110

5.4.1 Simulation Environment . . . . . . . . . . . . . . . . . 110

5.4.2 CRs achieved data rates and satisfaction . . . . . . . . . 111

Conclusions 114

Bibliography 115

Appendix 127

A List of Acronyms 127

III

Introduction 1

Introduction

In recent years there has been an increasing demand for high data rate wire-

less applications. Robust high data rate communications face several challenges.

Specifically, transmission of high rate information typically experiences high de-

lay spread in wireless environments.

Orthogonal Frequency Division Multiplexing (OFDM) is a transmission tech-

nique with high robustness in environments with large delayspread. In partic-

ular, an OFDM system is based on the idea of dividing the available bandwidth

into narrow sub-channels and sending low data rate signals in parallel on them.

The total high data rate is assured by the parallel transmissions. In this way, the

duration of symbol time becomes longer then in a Single Carrier (SC) scheme,

and OFDM turns out to be more robust against Inter-Symbol Interference (ISI)

caused by delay spread than SC scheme.

However, performance of OFDM systems is affected by channelestimation,

mobility and frequency offset at the local oscillator (LO).Channel estimation is a

well known issue to be solved to have an acceptable receptionquality. Moreover,

mobility introduces time-variations, which can make the link less reliable. Due

to the expansion of symbol length, OFDM systems are sensitive to time varia-

tions. In fact, time variations may introduce Inter-Carrier-Interference (ICI). Fur-

thermore, frequency offset at the local oscillator introduces ICI as well, which

further degrades the performance. Therefore to ensure reliable communication,

designing a robust channel estimation and ICI mitigation becomes essential.

In the first part of this thesis, the principles and the features of OFDM with

Introduction 2

a solution for channel estimation and inter-carrier interference mitigation are

presented. The proposed algorithm is explained referring to the Expectation-

Maximization (EM) paradigm.

Despite the OFDM sensitivity to ICI, OFDM is robust in high delay spread

caused by multipath environment and eliminates the needs ofequalizing the ef-

fect of delay spread. This feature allows high data rates andhas resulted in the

selection of OFDM as a standard for Digital Audio Broadcasting (DAB) [1], Dig-

ital Video Broadcasting (DVB) [2], Wireless Fidelity (WiFi),Worldwide Inter-

operability for Microwave Access (WiMax), and the 3rd Generation Partnership

Project (3GPP) Long Term Evolution (LTE). Moreover, it is being considered

as a potential technology for the promising Cognitive Radio (CR)Networks.

Specifically, cognitive radio technology is proposed for opportunistic spectrum

access as one of the enabling technologies for an efficient radio spectrum utiliza-

tion.

In the second part of the thesis, the application of OFDM to cognitive radio

networks is developed. In particular, specific features of OFDM signals are ex-

ploited to handle some issues in cognitive radio networks. Bydefinition, a CR

is capable to change its transmitter parameters and interacting with the environ-

ment. In more details, a CR can utilize the band, licensed to specific services, on

the condition that it has to vacate the spectrum as soon as thePrimary User (PU)

is detected. To this purpose, several sensing techniques are commonly used to

detect PU signals. It has to be noticed that OFDM signals exhibit periodicities

embedded in equally spaced sinusoidal carriers, cyclic prefix (CP) and pilot pat-

terns that can be exploited not only to detect but also to classify heterogeneous

PUs. Following this reasoning, a better knowledge of the environment is avail-

able. We also exploit the ability of classifying heterogeneous PUs to improve the

CR adaptability in terms of throughput and interference protection towards PUs,

and to design a flexible Cognitive Radio Resource Management (RRM).

Introduction 3

Dissertation outline

This research is devoted to the analysis of OFDM systems and application to

cognitive radio networks. The work consists in two parts: inthe first part, the

OFDM technique is presented highlighting the OFDM sensitivity to inter-carrier

interference. The proposed solution to mitigate the interference among subcar-

riers is then described. In the second part, the OFDM application to cognitive

radio networks is introduced, showing how to exploit the specific features of

OFDM signals to deal with some issues in cognitive radio networks.

The first part of the thesis, dealing with OFDM Systems and ICI mitigation,

consists inChapter 1, andChapter 2.

Chapter 1describes the principles and features of OFDM Systems. First,

a brief overview on wireless propagation is given. Then, theOFDM technique

is analyzed highlighting benefits and drawbacks. In particular, we focus on the

robustness of OFDM to multipath channels and its sensitivity to the inter-carrier

interference.

Chapter 2analyzes the effects of ICI in more details and proposes a solution

for ICI Mitigation. In particular, it is possible to represent this interference in

the frequency domain by means of an ICI matrix, whose estimation is crucial

to ensure reliable communication. Specifically, we proposean iterative method

to mitigate the interference among subcarriers exploitingthe presence of pilot

tones in the frequency domain. We describe the proposed method considering

the EM paradigm. The proposed technique is very effective inmultipath slow

fading channels with frequency offset at local oscillator and a simplified version

of it is also introduced in case of AWGN channel with frequencyoffset at the

local oscillator. Simulation results show that the presented algorithm converges

very quickly and looks promising to be used in actual implementations.

Introduction 4

The second part of the thesis, dealing with the application of OFDM to cog-

nitive radio network, consists inChapter 3, Chapter 4, andChapter 5.

Chapter 3presents an overview of cognitive radio networks. First, webriefly

describe the cognitive radio technology. Then, the physical architecture of CR

equipments and the network architectures on licensed band and on unlicensed

band are presented. The spectrum management framework is also analyzed.

The functionalities of the spectrum management are explained in more details,

i.e. spectrum sensing, spectrum decision, spectrum sharing, spectrum mobility.

More emphasis is given to spectrum sensing that is at the basis of the following

Chapter 4. We also briefly investigate how CR features influence the perfor-

mance of the upper layer protocols, i.e., Medium Access Control (MAC), rout-

ing, and transport, respectively. Finally, the current effort on CR standardization

are summarized.

Chapter 4shows how we exploit OFDM features in CR networks. The fea-

tures of OFDM signals have been employed to recognize heterogeneous PUs in

order to improve CR adaptability. Moreover, the ability of classify heterogeneous

PUs has been used to adapt CR transmission parameters to the environment in

the most efficient way. In fact, after classifying OFDM PU signals, the charac-

teristics of each PU type, the allowed interference level, the bandwidth and the

idle time, are extracted and exploited for CR adaptability effects. According to

this, a new CR throughput/interference adapter is proposed.The CR throughput

is efficiently increased depending on the specific features of PU types.

Chapter 5proposes an efficient Cognitive Radio Resource Management (CRRM),

which exploits the ability of classifying heterogeneous PUs. In this context, the

RRM calculates the available capacity of CR network based on thedifferent

PU types identified by CRs. In particular, we propose an RRM control mecha-

Introduction 5

nism to regulate the sharing of total available capacity among CRs. Specifically,

the existence of a specific PU type in a CR network influences theamount of

available capacity for the CRs. A cluster of CRs that share the same available

capacity is formulated based on the influencing PU type(s). As a result, the pro-

posed RRM identify different PU types with their associated CR clusters. The

proposed RRM is comprised of two stages: first, the RRM assigns CRs totheir

appropriate clusters based on CRs demands and available capacity in the cluster

(Admission Control Policy); then, the RRM allocates the required resources for

the newly assigned CRs to the cluster based on Orthogonal Frequency Division

Multiplexing Access (OFDMA) technique.

An Optimization framework for cognitive RRM that exploits multiple fea-

tures of heterogeneous PUs is proposed. The objective of theoptimization frame-

work is to minimize the difference between the available capacity and the achiev-

able data rate while satisfying CR demands and interference constraints. A Sub-

optimal solution for cognitive RRM that requires feasible computational require-

ments is also proposed.

Finally, Conclusions and future worksare discussed by showing some direc-

tions to improve the proposed solutions.

Conclusions 114

Conclusions

OFDM is a popular scheme for wideband digital communication, used in several

applications such as DAB, DVB, WiFi, WiMax, and LTE. Moreover,OFDM

is being considered as a potential technology for the promising cognitive radio

networks. In this thesis, the inter-carrier interference effects on OFDM are dis-

cussed.

In the first part of the thesis a solution for ICI Mitigation in OFDM systems

is proposed. Simulation results show the effectiveness of the proposed solution,

and the simplified version of it, in different scenarios. Moreover, it is shown that

the algorithm converges after few iterations.

In the second part of the thesis, the application of OFDM to cognitive ra-

dio networks is presented. Specifically, after a brief overview of cognitive ra-

dio networks, it is shown how to exploit the specific featuresof OFDM signals

to recognize heterogeneous primary users in order to improve cognitive radio

adaptability. Performances are derived by evaluating the behavior of the CAF

Detector/Classifier and of the CR throughput adapter. Simulation results show

how CR throughput depends on each feature of heterogeneous PUsignals. More-

over, a Cognitive Radio Resource Management is developed by exploiting het-

erogeneous primary users and an OFDMA based resource allocation. The perfor-

mance of the proposed algorithm is evaluated in terms of total data rate achieved

by the CR users, and the satisfaction of CR users.

Bibliography 115

Bibliography

[1] European Telecommunications Standards Institute (ETSI), Radio Broad-

casting Systems; Digital Audio Broadcasting (DAB) to Mobile,Portable

and Fixed Receivers, European Telecommunication Standard ETS 300 401,

1st edition, reference DE/JTC-DAB, Feb.1995, http://webapp.etsi.org/.

[2] European Telecommunications Standards Institute (ETSI), Digital Video

Broadcasting (DVB); Framing Structure, Channel Coding andModula-

tion for Digital Terrestrial Television, European Telecommunications Stan-

dard, ETS 300 744 1st edition, reference DE/JTC-DVB-8, Mar.1997,

http://webapp.etsi.org/.

[3] H. Lui, G. Li, OFDM-Based BroadbandWireless Networks Design and Op-

timization, JOHN WILEY& SONS, Hoboken, New Jersey, 2005

[4] J. G. Proakis, Digital Communications, McGraw-Hill, NewYork, 2001.

[5] M. C. Jeruchim, P. Balaban, K. S. Shanmugan, Simulation of Communi-

cation Systems, Modelling, Methodology, and Techniques, cap. 9, Kluwer

Academic Publishers, New York, 2002

[6] Chang,R. W., “Orthogonal Frequency Division Multiplexing", U.S. Patent,

3,488,445, filed 1966, issued Jan. 6 1970.

[7] S. Weinstein and P. Ebert, “Data transmission by Frequency-Division Mul-

tiplexing using the Discrete Fourier Transform",IEEE Transactions on

Communications, 628-634, 19(5), 1971.

Bibliography 116

[8] Cimini, “Analysis and simulation of a digital mobile channel using orthog-

onal frequency division multiplexing",IEEE Transactions on Communica-

tions, vol. COMM-33, pp. 665-675, July 1985.

[9] Reto Ness, Jean-Paul Linnartz, Liesbet Van der Perre, Marc Engels, Wire-

less OFDM Systems: How to Make ThemWork,Mark Engels, ed., Chapter

3, pp. 33-51, Kluwer Academic Publishers, Dordrecht, The Netherlands,

2002.

[10] Q. Shengping, Y. Changchuan, L. Jianfeng, Y. Guangxin, “Pilot-Symbol-

Aided Frequency Offset Estimation and Correction for OFDM System,”

14th IEEE 2003 International Symposium on Personal, lndoorand Mobile

Radio Communication Proceedings (PIMRC’03).

[11] T. M. Schmidl and D. C. Cox, “Robust Frequency and Timing Synchroniza-

tion for OFDM,” IEEE Transactions on Communications, pp. 1613-1621,

Vol. 45, No. 12, Dec. 1997.

[12] M. Morelli and U. Mengali, “An Improved Frequency Offset Estimator for

OFDM Applications,”IEEE Communications Letters, pp. 75-77, vol. 3, no.

3, Mar. 1999.

[13] J.-D. Kim, Y.-S. Byun, “A New Inter-Carrier InterferenceCancellation Us-

ing CP-ICA Scheme in OFDM Systems,” IEEE 65th Vehicular Technology

Conference, 2007. VTC2007-Spring, Dublin (Ir), April 22-25,2007, pp.

2369 - 2373.

[14] P. H. Moose, “A Technique for Orthogonal Frequency Division Multiplex-

ing Frequency Offset Correction,”IEEE Transactions on Communications,

vol. 42, no. 10, pp. 2908-2914, Oct. 1994.

[15] T. K. Moon “The Expectation- Maximization Algorithm,”IEEE Signal

Processing Magazine, Vol. 13, November 1996.

Bibliography 117

[16] B. Xiangyuan, J. Huiying and A. Jianping, “Iterative Detection of OFDM

signals in dispersive fading channels,”International Conference on Wire-

less Communications, Networking and Mobile Computing, WiCOM,Sept

22-24, 2006.

[17] S. Jain, P. Gupta and D. K. Mehra, “EM-MMSE based Channel Estimation

for OFDM Systems,” inProc. IEEE, I.C.I.T., 2006, Mumbai, pp. 2598-

2602.

[18] J. H. Lee, J. C. Han and S. C. Kim, “Joint Carrier Frequency Synchroniza-

tion and Channel Estimation for OFDM Systems Via the EM Algorithm,”

IEEE Transaction on Vehicular Technology, Vol. 55, No. 1, January 2006.

[19] X. Li and J. A. Ritcey, “Maximum-Likelihood Estimation of OFDM Carrier

Frequency Offset for Fading Channels,” inProceedings of the Thirty-First

Asilomar Conference on Systems & Computers, Los Alamitos, CA, USA,

Nov 1997, Vol. 1, pp. 57 - 61.

[20] R. R. Lopes and J. R. Barry, “Blind Iterative Channel Identification and

Equalization,”IEEE International Conference on Communication, vol. 7,

pp. 2256-2260, June 2001.

[21] C. A. Montemayor, P. K. Flikkema, “Near-Optimum Iterative Estimation of

Dispersive Multipath Channels,”IEEE Vehicular Technology Conference,

vol. 3, pp. 2246-2250, 1998.

[22] L. Favalli, P. Savazzi, A. Vizziello, “Frequency Domain estimation and

Compensation of Intercarrier Interference in OFDM Systems,” accepted

for publication in10th International Symposium on Spread Spectrum Tech-

niques and Application (ISSSTA 2008), Bologna, August 2008.

[23] L. Favalli, P. Savazzi, A. Vizziello, “Estimation and Mitigation of Inter-

carrier Interference for OFDM Systems in Multipath Fading Channels,”

accepted for publication in4th IEEE International Conference on Wireless

Bibliography 118

and Mobile Computing, Networking and Communications (WiMob 2008),

Avignon, October 2008.

[24] J. Armstrong, “Analysis of new and existing methods of reducing intercar-

rier interference due to carrier frequency offset in OFDM,”IEEE Transac-

tions on Communications, vol. 47, no. 3, pp. 365-369, March 1999.

[25] H.-C. Wu, “Analysis and characterization of intercarrier and interblock in-

terferences for wireless mobile OFDM systems,”IEEE Transactions on

Broadcasting, vol. 52, no. 2, June 2006.

[26] L. Rugini, P. Banelli, S. Cacopardi, “Probability of Errorof OFDM Systems

with Carrier Frequency Offset in Frequency-Selective Fading Channels,”

IEEE ICC 2004, Paris, Fr, June 20-24, 2004 Vol.6, pp. 3289- 3293.

[27] G.J. McLachlan, T. Krishnan,The EM algorithm and extensions, Wiley

Interscience, 1997.

[28] P. Savazzi, P. Gamba, L. Favalli, “Joint Symbol Timing Recovery and

Equalization for Short Burst Transmissions,”IEEE Transaction on Vehicu-

lar Technology Conference VTC-2007 Fall, Sept. 30-Oct. 3, 2007.

[29] Y. Mostofi and D. C. Cox, “ICI Mitigation for Pilot-Aided OFDM Mobile

Systems,”IEEE Transactions on Wireless Communications, vol. 4, no. 2,

March 2005.

[30] R. Negi and J. Cioffi, “Pilot tone selection for channel estimation in a mo-

bile OFDM system,”IEEE Trans. Consumer Electron., Vol. 44, pp. 1122-

1128, Aug. 1998

[31] L.Rugini, P. Banelli, and G. Leus, “Simple Equalization of Time-Varying

Channels for OFDM,”IEEE Communication Letters, Vol. 9, No. 7, July

2005.

Bibliography 119

[32] M. J. Fernández-Gaetino García, O. Edfors, J. M. Páez-Borrallo, “Fre-

quency Offset Correction for Coherent OFDM in Wireless Systems,” in

IEEE Trans. Consum. Electron., vol. 47, no. 1, pp. 187-193, Feb. 2001.

[33] FCC, “Notice of proposed rule making and order,”ET Docket, N. 03-222,

Dec. 2003.

[34] DARPA XG WG, “The XG Architectural Framework V1.0,” 2003.

[35] DARPA XG WG, “The XG Vision RFC V1.0,” 2003.

[36] FCC, “Notice of proposed rule making and order,” ET DocketNo. 03-222,

Dec. 2003.

[37] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,”

IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp.

201-220, February 2005.

[38] R. Thomas, L. DaSilva, and A. MacKenzie, “Cognitive networks,” in Proc.

IEEE DySPAN 2005, November 2005, pp. 352-360.

[39] I. F. Akyildiz, W.-Y. Lee, and K. Chowdhury, “COGNITIVE RADIONET-

WORKS: Theory and Applications,” WILEY, in press.

[40] J. Mitola III, “Cognitive radio: an integrated agent architecture for soft-

ware defined radio,”Ph.D. dissertation, KTH Royal Institute of Technol-

ogy, 2000.

[41] F. Jondral, “Software-defined radio-basic and evolution to cognitive radio,”

EURASIP Journal on Wireless Communication and Networking, 2005.

[42] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mhanty “Nextgenera-

tion/dynamic spectrum access/cognitive radio wireless networks: a survey,”

Computer Networks, Elsevier, Vol. 50, no. 13, pp. 2127-2159, Sept. 2006.

Bibliography 120

[43] D. Cabric, S. M. Mishra, R. W., and Brodersen, “Implementation issues

in spectrum sensing for cognitive radios,”in Proc. of Asilomar Conf. on

Signals, Systems and Computers, November 2004, pp. 772-776.

[44] D. Cabric and R. Brodersen, “Physical layer design issues unique to cog-

nitive radio systems,”in Proc. IEEE Persona Indoor and Mobile Radio

Communications(PIMRC) 2005, September 2005.

[45] I. Akyildiz, Y. Altunbasak, F. Fekri, and R. Sivakumar, “Adaptnet: adaptive

protocol suite for next generation wireless internet,”IEEE Communications

Magazine, vol. 42, no. 3, pp. 128-138, 2004.

[46] B. Razavi, RF Microelectronics.Prentice Hall, 1997.

[47] I. F. Akyildiz, W.-Y. Lee, K. Chowdhury, “Spectrum management in cog-

nitive radio ad hoc networks,”IEEE Network, vol. 23, no. 4, pp. 6-12, July-

August 2009.

[48] I. F. Akyildiz, W. Y. Lee, and K. Chowdhury, “CRAHNs: Cognitive radio

ad hoc networks,”Ad Hoc Network Journal (Elsevier), vol. 7, no. 5, pp.

810836, July 2009.

[49] A. Al Daoud, M. Alanyali, and D. Starobinski, “Secondary pricing of spec-

trum in cellular cdma networks,”in New Frontiers in Dynamic Spectrum

Access Networks, 2007, DySPAN 2007, April 2007, pp. 535-542.

[50] C.-T. Chou, N. Sai Shankar, H. Kim, and K. Shin, “What and howmuch

to gain by spectrum agility?”Selected Areas in Communications, IEEE

Journal on, vol. 25, no. 3, pp. 576-588, April 2007.

[51] H. Kim and K. Shin, “Fast discovery of spectrum opportunities in cognitive

radio networks,”in New Frontiers in Dynamic Spectrum Access Networks,

2008, DySPAN 2008, Oct. 2008, pp. 1-12.

Bibliography 121

[52] H. Kim and K. Shin, “Efficient discovery of spectrum opportunities with

mac-layer sensing in cognitive radio networks,”Mobile Computing, IEEE

Transactions on, vol. 7, no. 5, pp. 533-545, May 2008.

[53] W.-Y. Lee and I.F. Akyildiz “Optimal Spectrum Sensing Framework for

Cognitive Radio Networks,”IEEE Transaction on Wireless Communica-

tions, Vol. 7, pp. 3845-3857, Oct. 2008.

[54] J. Zhao, H. Zheng, and G.-H. Yang, “Distributed coordination in dynamic

spectrum allocation networks,”in Proc. IEEE DySPAN 2005, Nov. 2005.

[55] K. Sriram and W. Whitt, “Characterizing superposition arrival processes in

packet multiplexers for voice and data,”Selected Areas in Communications,

IEEE Journal on, vol. 4, no. 6, pp. 833-846, Sep 1986.

[56] D. Willkomm, S. Machiraju, J. Bolot, and A. Wolisz, “Primary users in

cellular networks: A large-scale measurement study,”in New Frontiers in

Dynamic Spectrum Access Networks, DySPAN 2008, Oct. 2008, pp. 1-11.

[57] S. Geirhofer, L. Tong, and B. Sadler, “Cognitive radios for dynamic spec-

trum access - dynamic spectrum access in the time domain: Modeling and

exploiting white space,”Communications Magazine, IEEE, vol. 45, no. 5,

pp. 66-72, May 2007.

[58] R. Jain and S. Routhier, “Packet trains-measurements anda new model

for computer network traffic,”Selected Areas in Communications, IEEE

Journal on, vol. 4, no. 6, pp. 986-995, Sep 1986.

[59] V. Paxson and S. Floyd, “Wide area traffic: the failure ofpoisson model-

ing,” Networking, IEEE/ACM Transactions on, vol. 3, no. 3, pp. 226-244,

Jun 1995.

[60] M. Oner and F. Jondral, “On the extraction of the channelallocation infor-

mation in spectrum pooling systems,”IEEE Journal on Selected Areas in

Communications, vol. 25, no. 3, pp. 558-565, April 2007.

Bibliography 122

[61] S. M. Mishra, A. Sahai, and R. W. Brodersen, “Cooperative sensing among

cognitive radios,”in Proc. IEEE ICC 2006, June 2006, pp. 1658-1663.

[62] B. Wild and K. Ramchandran, “Detecting primary receiversfor cognitive

radio applications,”in Proc. IEEE DySPAN 2005, November 2005, pp. 124-

130.

[63] FCC, “Notice of inquiry and notice of proposed rulemaking,” ET Docket

No. 03-237, November 2003.

[64] V. Brik, E. Rozner, S. Banarjee, and P. Bahl, “DSAP: a protocol for coor-

dinated spectrum access,”in Proc. IEEE DySPAN 2005, Nov. 2005.

[65] C. Raman, R. Yates, and N. Mandayam, “Scheduling variable rate links via

a spectrum server,”in Proc. IEEE DySPAN 2005, Nov. 2005.

[66] S. Zekavat and X. Li, “User-central wireless system: ultimate dynamic

channel allocation,”in Proc. IEEE DySPAN 2005, Nov. 2005.

[67] L. Cao and H. Zheng, “Distributed spectrum allocation via local bargain-

ing,” in Proc. IEEE Sensor and Ad Hoc Communications and Networks

(SECON) 2005, Sept. 2005.

[68] J. Huang, R. Berry, and M. Honig, “Spectrum sharing with distributed in-

terference compensation,”in Proc. IEEE DySPAN 2005, Nov. 2005.

[69] L. Ma, X. Han, and C.-C. Shen, “Dynamic open spectrum sharing MAC

protocol for wireless ad hoc network,”in Proc. IEEE DySPAN 2005, 2005.

[70] S. Sankaranarayanan, P. Papadimitratos, A. Mishra, and S. Hershey, “A

bandwidth sharing approach to improve licensed spectrum utilization,” in

Proc. IEEE DySPAN 2005, 2005.

[71] Q. Zhao, L. Tong, and A. Swami, “Decentralized cognitive MAC for dy-

namic spectrum access,”in Proc. IEEE DySPAN 2005, Nov. 2005.

Bibliography 123

[72] H. Zheng and L. Cao, “Device-centric spectrum management,” in Proc.

IEEE DySPAN 2005, Nov. 2005.

[73] C. Cormio and K. R. Chowdhury, “A survey on MAC protocols for cogni-

tive radio networks,”Ad Hoc Networks (Elsevier) Journal, vol. 7, no. 7, pp.

1315-1329, September 2009.

[74] S.-Y. Lien, C.-C. Tseng, and K.-C. Chen, “Carrier sensing based multiple

access protocols for cognitive radio networks,”in Proc. IEEE International

Conference on Communications (ICC), May 2008, pp. 3208-3214.

[75] C. Cordeiro, K. Challapali, D. Birru, and S. Shankar, “IEEE 802.22: The

first world-wide wireless standard based on cognitive radios,” in Proceed-

ings of IEEE DySPAN, Nov 2005.

[76] C. Cordeiro, K. Challapali, and M. Ghosh, “Cognitive PHY andMAC lay-

ers for dynamic spectrum access and sharing of tv bands,”in Proceedings

of IEEE International Workshop on Technology and Policy forAccessing

Spectrum, Aug. 2006.

[77] C. Zhou and C. Chigan, “A game theoretic dsa-driven mac framework for

cognitive radio networks,”in IEEE International Conference on Communi-

cations (ICC), May 2008.

[78] P. Pawelczak, R. V. Prasad, L. Xia, and I. G. M. M. Niemegeers, “Cognitive

radio emergency networks - requirements and design,”in Proceedings of

IEEE DySPAN 2005, Nov. 2005.

[79] L. Ma, C.-C. Shen, and B. Ryu, “Single-radio adaptive channel algorithm

for spectrum agile wireless ad hoc networks,”in Proc. IEEE DySPAN 2007,

April 2007, pp. 547-558.

[80] J. Jia, J. Zhang, and Q. Zhang, “Cooperative relay for cognitive radio net-

works,” in Proc. IEEE INFOCOM 2009, April 2009, pp. 2304-2312.

Bibliography 124

[81] C. Cordeiro and K. Challapali, “C-MAC: A cognitive MAC protocol for

multi-channel wireless networks,”in IEEE DySPAN 2007, April 2007, pp.

147-157.

[82] B. Hamdaoui and K. G. Shin, “OS-MAC: an efficient MAC protocol for

spectrum-agile wireless networks,”IEEE Transactions on Mobile Comput-

ing, vol. 7, no. 8, pp. 915 - 930, August 2008.

[83] Y. R. Kondareddy and P. Agrawal, “Synchronized MAC protocol for multi-

hop cognitive radio networks,”in IEEE International Conference on Com-

munications (ICC), May 2008.

[84] H. Su and X. Zhang, “Opportunistic MAC protocols for cognitive radio

based wireless networks,”in Proceedings of Annual Conference on Infor-

mation Sciences and Systems, Mar. 2007.

[85] I. F. Akyildiz, W. Y. Lee, M. C. Vuran, and M. Shantidev, “Asurvey on

spectrum management in cognitive radio networks",IEEE Communica-

tions Magazine, vol. 46, no. 4, pp.4048, April 2008.

[86] IEEE 802 LAN/MAN Standards Committee, “IEEE 802.22 Stan-

dard."[Online]. Available: http://www.ieee802.org/22/

[87] IEEE Standards Coordinating Committee 41 (Dynamic Spectrum

Access Networks), “IEEE 1900 Standard.” [Online]. Available:

http://www.scc41.org/crinfo

[88] L. Bixio, G. Oliveri, M. Ottonello, C. S. Regazzoni, “OFDM recognition

based on cyclostationary analysis in an Open Spectrum scenario,” IEEE

69th Vehicular Technology Conference, Apr. 26-29 2009.

[89] K. Muraoka, M. Ariyoshi,and T. Fujii “A Novel Spectrum-Sensing Method

Based on Maximum Cyclic Autocorrelation Selection for Cognitive Radio

System,”IEEE Symposium on New Frontiers in Dynamic Spectrum Access

Networks, DySPAN 2008, Oct. 14-17 2008.

Bibliography 125

[90] C. R. Stevenson, C. Cordeiro, E. Sofer, and G. Chouinard, “Functional

Requirements for the 802.22 WRAN Standard,”IEEE 802.22-05/0007r46,

Sept. 2005.

[91] FCC, “Establishment of interference temperature metricto quantify and

manage interference and to expand available unlicensed operation in certain

fixed mobile and satellite frequency bands,”ET Docket03-289, Notice of

Inquiry and Proposed Rulemaking, 2003.

[92] T. C. Clancy, “Formalizing the Interference TemperatureModel,"’ Wiley

Journal on Wireless Communications and Mobile Computing, Vol. 7, Issue

9, pp. 1077-1086, May 2007.

[93] B. Canberk, I. F. Akyildiz, and S. Oktug, “Primary User Activity Modeling

Using First-Difference Filter Clustering and Correlation inCognitive Radio

Networks,”to appear in IEEE/ACM Transactions on Networking, 2010.

[94] N. Baldo, and M. Zorzi, “Fuzzy Logic for Cross-Layer Optimization in

Cognitive Radio Networks,” inIEEE Communications Magazine, April,

2008, pp.64-71.

[95] M. Petrova, and P. Mähönen, “Cognitive Resource Manager:a cross-layer

architecture for implementing cognitive radio networks,”in: F. Fittzek, M.

Katz (Eds.), Cognitive Wireless Networks, Springer, 2007.

[96] http://www-tkn.ee.tu-berlin.de/research/trace/trace.html.

[97] R. Iyengar, K. Kar, and B. Sikdar, Scheduling Algorithms for PMP Opera-

tion in IEEE 802.16 Networks, RAWNET 2006 workshop, in Conjunction

with WiOPT ’06, Boston, Ma.

[98] C. Mohanram and S. Bhashyam, “A sub-optimal joint subcarrier and power

allocation algorithm for multiuser OFDM,”IEEE Communications Letters,

vol. 9, no. 8, pp. 685-687, 2005.

126

Appendix

APPENDIX A. LIST OF ACRONYMS 127

Appendix A

List of Acronyms

3GPP 3rd Generation Partnership Project

A/D analog-to-digital

API Application Programming Interface

AWGN Additive White Gaussian Noise

BER Bit Error Rate

BS Base Station

BTS Base station Transceiver System

CA complex addition

CAF Cyclostationary Autocorrelation Function

CCC common control channel

CD complex division

CE Consumer Electronics

CIR Channel Impulse Response

CM complex multiplication

C-MAC Cognitive MAC

ComSoc Communications Society

CP Cyclic Prefix

CR Cognitive Radio

CRAHN Cognitive radio ad hoc network

CRM Cognitive Resource Manager

CRRM Cognitive Radio Resource Management

DAB Digital Audio Broadcasting

DARPA Defense Advanced Research Projects Agency

APPENDIX A. LIST OF ACRONYMS 128

DFT Discrete Fourier Transform

DVB Digital Video Broadcasting

EM Expectation-Maximization

EMC Electromagnetic Compatibility

ETSI European Telecommunications Standards Institute

FCC Federal Communications Commission

FDM Frequency Division Multiplexing

FFT Fast Fourier Transform

GI guard interval

HTTP Hypertext Transfer Protocol

ICI Inter-Carrier Interference

ICT Information and Communication Technology

IDFT Inverse Discrete Fourier Transform

IEEE Institute of Electrical and Electronic Engineers

IFFT Inverse Fast Fourier Transform

iid independent and identically distributed

ISI Inter-Symbol Interference

ISM industrial scientific and medical

LAN Local Area Network

LNA Low Noise Amplifier

LO local oscillator

LOS Line-Of-Sight

LTE Long Term Evolution

LTV Linear Time-Variant

MAC Medium Access Control

MAN Metropolitan Area Network

MMSE minimum mean square error

ML maximum likelihood

MPEG Moving Picture Experts Group

NO-VRR No-variable data rate requirements

NP-hard non-deterministic polynomial-time hard

OFDM Orthogonal Frequency Division Multiplexing

OFDMA Orthogonal Frequency Division Multiplexing Access

OS-MAC Opportunistic Spectrum MAC

PDP Power Delay Profile

PHY PHYsical layer

APPENDIX A. LIST OF ACRONYMS 129

POMDP Partially Observable Markov Decision Process

PU Primary User

QOS Quality Of Service

QPSK Quaternary Phase-Shift Keying

RF Radio Frequency

rms root-mean squared

RRM Radio Resource Management

RRS Reconfigurable Radio Systems

RTT round trip time

SC Single Carrier

SDR Software Defined Radio

SNR Signal to Noise Ratio

SYN-MAC Synchronized MAC

TAG Technical Advisory Group

TC Technical Committee

TCP Transmission Control Protocol

TGaf Task Group af

UDP User Datagram Protocol

UHF Ultra High Frequency

VRR variable data rate requirements

WiFi Wireless Fidelity

WiMax Worldwide Interoperability for Microwave Access

WRAN Wireless Regional Area Network

WSSUS Wide-Sense Stationary Uncorrelated Scattering

xG NeXt Generation