track the best timing phase by minimizing a cost function. ..... 6.5 Power spectral density (PSD) measurements by FFT, FFT with han- ning window, and filterbank ...
FILTERBANK MULTICARRIER TECHNIQUES FOR COGNITIVE RADIOS
by Peiman Amini
A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
Department of Electrical and Computer Engineering The University of Utah December 2009
c Peiman Amini 2009 Copyright All Rights Reserved
THE UNIVERSITY OF UTAH GRADUATE SCHOOL
SUPERVISORY COMMITTEE APPROVAL of a dissertation submitted by
Peiman Amini
This dissertation has been read by each member of the following supervisory committee and by majority vote has been found to be satisfactory.
Chair:
Behrouz Farhang-Boroujeny
Cynthia Furse
Rong-Rong Chen
Keneth Stevens
Fr´ed´eric Noo
THE UNIVERSITY OF UTAH GRADUATE SCHOOL
FINAL READING APPROVAL
To the Graduate Council of the University of Utah: I have read the dissertation of Peiman Amini in its final form and have found that (1) its format, citations, and bibliographic style are consistent and acceptable; (2) its illustrative materials including figures, tables, and charts are in place; and (3) the final manuscript is satisfactory to the Supervisory Committee and is ready for submission to The Graduate School.
Date
Behrouz Farhang-Boroujeny
Chair, Supervisory Committee
Approved for the Major Department
Marc Bodson Chair/Dean
Approved for the Graduate Council
David S. Chapman
Dean of The Graduate School
ABSTRACT The demand for wireless services is on the rise and the vast majority of the spectral resources have already been licensed. Consequently, cognitive radio technology has been proposed to make secondary use of licensed spectrum. Multicarrier communication technology has been suggested to utilize the white spaces in the spectrum. Orthogonal frequency-division multiplexing (OFDM) was the first multicarrier technique proposed for cognitive radios. However, OFDM suffers from significant leakage among the carriers of different users. On the other hand, filterbank multicarrier (FBMC) communication can overcome the spectral leakage. Therefore, FBMC has been suggested as an alternative to OFDM for cognitive radios. In this dissertation, we investigate the implementation issues that need to be addressed for an actual deployment of FBMC. Efficient polyphase structures for implementation of FBMC are investigated. A novel formulation for a family of polyphase structures for staggered modulated multitone (SMT) is derived. Using our derivation, it is shown that some of the SMT analysis structures in the literature are not applicable to frequency selective channels. A preamble design and related algorithms are proposed for FBMC systems. The proposed preamble is used to detect the beginning of packet, to adjust an automatic gain control, to synchronize the carrier frequency and timing phase, and to identify the channel impulse response. Furthermore, decision directed carrier and timing tracking algorithms are proposed to track residual timing and carrier offset after the acquisition. In addition, a decision directed phase lock loop (PLL) is designed to force any built up phase error to zero. Also, an algorithm is implemented to track the best timing phase by minimizing a cost function. This dissertation also reports implementation of a cognitive radio equipped with
filterbank spectrum sensing. The cognitive radio was implemented on a software defined radio platform. The filterbank spectrum sensor is shown to exhibit superior performance in terms of the spectral dynamic range when compared to the FFT based techniques. The radio can detect the presence of interferers on the carrier that is currently using and move to an unoccupied part of the spectrum. FBMC is also applied to fault detection on live wires. Optimally designed synthesis filterbanks are used to confine the test signal to the portion(s) of the frequency band that are free of live signal. Moreover, optimal analysis filters are designed which can separate the reflected test tones and minimize leakage from the live wire signals.
v
I dedicated this work to my family: Masoud, Mahin, Payvand, and Pooyan.
CONTENTS ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
iv
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
x
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii CHAPTERS 1.
2.
3.
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.1 Multicarrier Communications for Cognitive Radios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 OFDM/FFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Filterbank Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Filterbank Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Staggered Modulated Multitone, SMT . . . . . . . . . . . . . . . . . . . 1.4.2 Cosine Modulated Multitone, CMT . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Filtered Multitone, FMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.4 A Comparison of FMT, SMT and CMT . . . . . . . . . . . . . . . . . . 1.5 Contribution of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 7 9 12 12 12 13 13 13 16
A REVIEW ON FILTERBANK MULTICARRIER COMMUNICATION SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
2.1 Filterbank Multicarrier Communications . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Staggered Modulated Multitone, SMT . . . . . . . . . . . . . . . . . . . 2.1.2 Cosine Modulated Multitone, CMT . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Filtered Multitone, FMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 FBMC Sensitivity to Synchronization Errors . . . . . . . . . . . . . . . . . . . 2.2.1 SMT Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 CMT Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Sensitivity to Carrier Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Sensitivity to Timing Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20 20 25 29 29 30 32 34 39 42
POLYPHASE IMPLEMENTATION OF FILTERBANK MULTICARRIER COMMUNICATION SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1 Polyphase Synthesis Filterbank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Polyphase Analysis Filterbank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.3 SMT Polyphase Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.
5.
6.
3.3.1 Type-I SMT Polyphase Structure . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Type-II SMT Polyphase Structure . . . . . . . . . . . . . . . . . . . . . . 3.3.3 Type-III SMT Polyphase Structure . . . . . . . . . . . . . . . . . . . . . . 3.3.4 Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 CMT Polyphase Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51 54 57 66 68 71 72
PREAMBLE DESIGN FOR FILTERBANK MULTICARRIER SYSTEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
4.1 4.2 4.3 4.4 4.5 4.6
Preamble Design in OFDM Systems . . . . . . . . . . . . . . . . . . . . . . . . . FBMC Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carrier Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timing Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Sensitivity Discussion for SMT and CMT . . . . . . . . . . . . . . . . . 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75 78 79 83 84 85 90 90
CARRIER AND TIMING OFFSET TRACKING . . . . . . . . . . .
91
5.1 Literature Survey on FBMC Bind Synchronization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Carrier Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 SMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 CMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Timing Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91 94 94 95 98 99 103
COGNITIVE RADIO TESTBED . . . . . . . . . . . . . . . . . . . . . . . . . . 105 6.1 Problem Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Technical Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Channel Sensing and MAC Layer . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Implementation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Spectrum Sensing Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Test and Demonstration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.
106 107 108 110 110 111 113 117 118 120
FILTERBANK MULTICARRIER FOR COGNITIVE LIVE WIRE TESTING . . . . . . . . . . . . . . . . . . . . . . 121 7.1 In-Band and Out-of-Band Interference . . . . . . . . . . . . . . . . . . . . . . . 122 7.2 Filterbank MCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 7.2.1 Signal Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 viii
7.2.1.1 An Analysis of the Raised-Cosine Window Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1.2 An Optimum Choice of the Window Function . . . . . . . . . 7.2.1.3 The Relationship Between Time-Domain Windowing and Filterbank Synthesis . . . . . . . . . . . . . . . . 7.2.2 Analysis Filterbanks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Cognitive Live Wire Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.
129 130 132 133 135 136
CONCLUSIONS AND FUTURE RESEARCH . . . . . . . . . . . . . . 137 8.1 Outlook into Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Mobile FBMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 MIMO FBMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.3 Pilot Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 Implementation of FBMC Communications . . . . . . . . . . . . . . . 8.1.5 Implementation of Filterbank Multicarrier Reflectometry . . . . .
140 140 141 141 142 142
APPENDICES A. ACRONYMS AND ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . 143 B. VARIABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
ix
LIST OF FIGURES 1.1 General structure for filterbank system. . . . . . . . . . . . . . . . . . . . . . . . .
6
1.2 Transmit windowing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
1.3 The power spectral density of a subcarrier of an OFDM signal when different choices of the roll-off parameter β is used. . . . . . . . . . . . . . . .
9
1.4 Receiver windowing for OFDM signal. . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 General structure of filterbank sensing. . . . . . . . . . . . . . . . . . . . . . . . . 11 1.6 Comparison between CMT, FMT and SMT . . . . . . . . . . . . . . . . . . . . . 14 2.1 Structure of the continuous-time SMT system. . . . . . . . . . . . . . . . . . . 21 2.2 Baseband CMT Trans-multiplexer. . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3 CMT modulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 The ICI, ISI, and signal energy of SMT when ∆f is 0.02 of carrier spacing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.5 The ICI, ISI, and signal energy of SMT when ∆f is 0.01 of carrier spacing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.6 The ICI, ISI, and signal energy of CMT when ∆f is 0.02 of carrier spacing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.7 The ICI, ISI, and signal energy of CMT when ∆f is 0.01 of carrier spacing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.8 Signal to interference ratio in the presence of carrier offset for SMT and CMT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.9 Distortion of SMT, CMT, and OFDM in the presence of carrier offset when SNR=10 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.10 Degradation of SMT, CMT, and OFDM in the presence of carrier offset when SNR=30 dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.11 The ICI, ISI, and signal energy of SMT when t0 is
2 T. 64
. . . . . . . . . . . 40
2.12 The ICI, ISI, and signal energy of SMT when t0 is
1 T. 64
. . . . . . . . . . . 40
2.13 The ICI, ISI, and signal energy of CMT when t0 is
1 T. 64
. . . . . . . . . . . 41
2.14 The ICI, ISI, and signal energy of CMT when t0 is
2 T. 64
. . . . . . . . . . . 41
2.15 Signal to interference ratio in the presence of timing offset for SMT and CMT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.16 Distortion versus normalized timing offset for SNR = 10 dB. . . . . . . . 43 2.17 Distortion versus normalized timing offset for SNR = 30 dB. . . . . . . . 44 3.1 General structure of a synthesis filterbank . . . . . . . . . . . . . . . . . . . . . . 47 3.2 Polyphase implementation of a synthesis filterbank. . . . . . . . . . . . . . . 48 3.3 Simplified structure for the polyphase synthesis filterbank. . . . . . . . . . 49 3.4 Block diagram of a discrete time analysis filterbank. . . . . . . . . . . . . . . 49 3.5 Polyphase analysis filterbank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.6 Simplified polyphase structure for analysis filterbanks. . . . . . . . . . . . . 51 3.7 A discrete time SMT transmitter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.8 Block diagram of type-I polyphase implementation of SMT transmitter. 53 3.9 A discrete time SMT receiver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.10 Block diagram of type-I polyphase implementation of SMT receiver. . 56 3.11 A combined discrete time SMT transmitter. . . . . . . . . . . . . . . . . . . . . 57 3.12 Block diagram of type-II polyphase implementation of SMT transmitter. 58 3.13 A combined discrete time SMT receiver. . . . . . . . . . . . . . . . . . . . . . . . 59 3.14 Block diagram of type-II polyphase implementation of SMT receiver. . 60 3.15 Block diagram of type-III polyphase implementation of SMT transmitter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.16 Block diagram of type-III polyphase implementation of SMT receiver.
67
3.17 Block diagram of polyphase implementation of CMT transmitter. . . . . 70 3.18 Block diagram of polyphase implementation of CMT receiver. . . . . . . 71 4.1 Packet format in IEEE 802.11a. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.2 The proposed packet format for FBMC systems. . . . . . . . . . . . . . . . . . 79 4.3 Power spectral density of the proposed long preamble. . . . . . . . . . . . . 80 4.4 The signal analyzer for timing acquisition. . . . . . . . . . . . . . . . . . . . . . . 84 4.5 Residual CFO of the proposed long preamble-based carrier acquisition methods. The vertical axis shows the MSE of residual CFO normalized to the subcarrier spacing of the payload. The horizontal axis indicates the SNR during the payload part of the packet. . . . . . . . . . . 88 4.6 SIR comparison of (4.23) and (4.24). The histogram are based on testing over 10000 randomly generated channels. . . . . . . . . . . . . . . . . . 89 5.1 A PLL equipped FBMC receiver. The input y[n] is the demodulated received signal. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.2 A PLL equipped SMT receiver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 xi
5.3 A PLL equipped CMT receiver. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.4 Performance of the PLL for carrier tracking in a CMT receiver. The top figure shows the phase error, ϕ[n], at the loop filter input. The lower figure shows the phase jitter, φ[n], of the input signal to the analysis filterbank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.5 Performance of the PLL for carrier tracking in an SMT receiver. The top figure shows the phase error, ϕ[n], at the loop filter input. The lower figure shows the phase jitter, φ[n], of the input signal to the analysis filterbank. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 5.6 Mean square error at the output of an SMT receiver, averaged over all subcarriers, with and without a timing tracking loop. . . . . . . . . . . . 102 5.7 Comparison of the MSE of CMT and SMT in tracking mode. . . . . . . . 103 5.8 Comparison of the MSE of CMT for three cases: CMT1 (KP = 0.1208, KI = 0.0068), CMT2 (KP = 0.0837 and KI = 0.001), CMT3 (KP = 0.0193 and KI = 0.0001), and SMT in tracking mode. . . . . . . . . . . . . . 104 6.1 Progressive simulation based design (PSBD) of a single cognitive modem. The implementation starts from the sensing component and progressively more of the simulated models (left dotted box) are implemented (right dotted box). The rectangles are DEVS models simulated, and parallelograms are implemented components on SDR . . . . 109 6.2 Transmitter data flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 6.3 Receiver data flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 6.4 The testbed setup for examining the performance of filterbank sensing.118 6.5 Power spectral density (PSD) measurements by FFT, FFT with hanning window, and filterbank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 7.1 An example of the test signal according to [1]. . . . . . . . . . . . . . . . . . . . 124 7.2 General structure of a filterbank MCR system . . . . . . . . . . . . . . . . . . 126 7.3 An example of the magnitude response of the window function w(t) and its factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7.4 Magnitude responses of H(f ) according to a raised-cosine design and prolate design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 7.5 An example of the test signal after applying w(t). The window function w(t) is based on a prolate design. . . . . . . . . . . . . . . . . . . . . . . 132 7.6 Magnitude response of the analysis prototype filter. . . . . . . . . . . . . . . 135
xii
ACKNOWLEDGMENTS Foremost, I want to thank my advisor, Dr. Behrouz Farhang-Boroujeny, for his guidance and patience. I could not finish this endeavor without his support. Thanks to Dr. Farhang, I learned not only my field of research but also various other subjects. I would like to acknowledge help and support of Dr. Cynthia Furse, Dr. Rong-rong Chen, Dr. Keneth Stevens and Dr. Fred Noo, members of my thesis committee. I thank Dr. Roland Kempter and Ehsan Azarnasab for their help and useful discussions on different areas of wireless communications and embedded programming. My brother, Pooyan, your support and your presence in Salt Lake City in the last two years of my PhD was extremely helpful. I would also like to express my thanks and gratitude to my great friends, Dr. Farhad Mahdavi, Sussan Saghei, Marieh Nasoori, Dr. Hossein Mirfakharai, Mehran Tahmasebi, and Farideh Bahremanpour for helping me through the most difficult times. My parents, Masoud and Mahin, I would have not achieved this if you had not set me on the path of learning and hard work. It should be mentioned that parts of this dissertation have been previously published in IEEE sensor journal, proceedings of 2005, 2006, and 2007 software defined radio technical conference, and proceeding of 2008 IEEE dynamic spectrum access conference. I acknowledge my funding sources, National Science Foundation and University of Utah Technology Commercialization Office. I would also like to thank the software and hardware support from Software Defined Radio Forum, Lyrtech, Texas Instruments, Xilinx, Mathworks, Greenhills, Prismtech, Zeligsoft, and Synplicity.
CHAPTER 1 INTRODUCTION The demand for ubiquitous wireless services has been on rise in the past and is expected to remain the same in the future. As a result, the vast majority of the available spectral resources have already been licensed. In the United States, government regulatory agencies have allocated different blocks of spectrum from 9 kHz to 300 GHz for various applications. Hence, it appears that the regulated radio spectrum has been fully occupied and new applications will not have access to the radio spectrum. However, spectrum bands need to be made available to the public even if they are already allocated or licensed. It has been noted that the static frequency allocations have resulted in inefficient usage of the spectrum resources. For example, measurements have shown that that the actual spectrum utilization in the 3-4 GHz frequency band is 0.5% and 0.3% in the 4-5 GHz [2]. In fact, most of the commercial wireless systems transmit intermittently and they need variable bandwidth over time. The advancement of digital signal processors (DSPs), general purpose processors (GPPs) and field-programmable gate arrays (FPGAs) has enabled us to build reconfigurable radios. These radios can change their configuration to meet the requirements of communication network they are operating on. This concept is the foundation of software defined radios where the radio functionality is mostly accomplished by software programming. In software defined radios (SDR), once the signal is digitized, the radio functions are implemented using software-driven components. Hence, the SDR technology has enabled us to build radios that can transmit over different frequency bands and change their modulation schemes and pulse shapes based on policies defined in software. Consequently, cognitive radio
2 technology has been proposed where the cognitive radio can share the spectrum with the incumbent licensed users. In other words, cognitive radio technology enables us to have secondary (i.e., unlicensed) users that are allowed to transmit and receive data over portions of the spectra when primary (i.e., licensed) users are inactive. This is done in a way that the secondary users (SUs) are invisible to the primary users (PUs). In such a setting, PUs are ordinary terminals within their base-station centric network or in direct point-to-point communications. PUs thus do not need to possess much intelligence beyond the ability to communicate with their peers in their networks. The SUs, on the other hand, should have the intelligence of sensing the spectrum and using the available resources when they need them. At the same time, the SUs need to give up the spectrum when a PU begins transmission. This emerging technology is being investigated and used by different research organizations and government agencies. The DARPA next generation program (XG) [3] and wireless network after next (WNAN) [4] programs have been studying the applicability of dynamic frequency-selective radios based on cognitive radio concepts. Agile radios dynamically adapt to the channel environment. These radios assess their environment and the spectrum policies and regulations and capitalize on the available spectrum in their environment. The enhancements in communication technology as well as the new requirements for the public safety agencies indicate that narrow-band real-time voice communications might not be sufficient for mission critical applications. The International Telecommunication Union (ITU) and National Emergency Number Association (NENA) are developing the multimedia location based communication requirements for inclusion in the next generation of public safety architectures [5]. Furthermore, in disaster scenarios such as earthquake or hurricane the communication infrastructure might be damaged and therefore ad-hoc cognitive wireless communication becomes more critical [6]. Therefore building radios which can dynamically adapt to the environment and transmit with high data rates can save lives and reduce the risks involved in the missions of public safety agencies [5]. The advancements in SDR technology, and the under-utilization of spectrum in
3 TV bands resulted in a decision by Federal Communications Commission (FCC) to open the TV spectrum band for cognitive use in 2004 [7]. IEEE 802.22 working group [7] is currently working on constructing Wireless Regional Area Networks (WRAN) which utilize the white spaces in the allocated TV spectrum. The 802.22 radio needs to operate on TV bands without interfering with the TV signals. Therefore the main challenge in the development of the first commercial cognitive radios is to ensure that the SUs are invisible to the PUs. To accomplish this, the SUs need to sense the spectrum, and this involves a spectral analysis. When the modulation scheme of the licensed signal is known, detection of the PUs can be performed through feature detection. This is the case in IEEE 802.22, which is currently being designed to operate in the TV bands. However, in the general case where the signal features might not be available, spectral analysis mainly relies on energy detection. Therefore, it is important that the spectrum sensing of SUs features high spectral dynamic range and frequency resolution. The other challenge in design of cognitive radios is building a radio that can efficiently access the available spectrum holes. The radio should be able to dynamically and effectively change its pulse-shape and fill in the spectrum holes. In the rest of this chapter we first talk about multicarrier communications and its applications to the cognitive radios. Then we present the orthogonal frequencydivision multiplexing and fast fourier transform (OFDM/FFT) system and study its performance in cognitive radio applications. Filterbank sensing and its performance are presented in Section 1.3. In Section 1.4, staggered modulated multitone (SMT), cosine-modulated multitone (CMT) and filtered multitone (FMT) are introduced as three filterbank communication methodologies. Finally, the contributions and organization of this dissertation are described.
1.1
Multicarrier Communications for Cognitive Radios
Multicarrier communication technology has been suggested as a suitable candidate to utilize the white spaces in the spectrum [8]. OFDM was the first multicarrier technique proposed for CRs. The rationale is that any cognitive radio needs to
4 sense the spectrum, and this involves some sort of spectral analysis. Since the fast Fourier transform (FFT) can be used for spectral analysis and at the same time act as the demodulator of an OFDM signal, OFDM is a suitable candidate for multicarrier-based cognitive radio systems. However, a number of shortcomings of OFDM in the application of cognitive radio have been noted in [9] and solutions to them have been proposed. To elaborate, the source of the problems with the OFDM solution is the large side-lobes of the frequency response of the filters that characterize the channels associated with subcarriers in an OFDM system. Therefore, there is significant interference among the carriers of different SUs as well as between SUs and PUs in the wireless channel. If the spectrum sensor lacks a sufficiently high spectral dynamic range, SUs may not be able to detect the low power PUs and they may interfere with them. Moreover, if the resolution of the spectrum sensing is low, the radio will not be able to best harness the wireless resources. It has been shown that FFT as part of an OFDM data transmission system is not able to provide a sufficiently high spectral dynamic range for channel sensing. On the other hand, as a channel sensing tool, filterbank-based spectrum analyzer can be applied to cognitive radios and its performance found to be close to that of the Thomsons multitaper method (MTM) [10], which has been proposed as the best candidate for cognitive radios [11]. A multicarrier transceiver is required to feature two major properties in a cognitive radio system where the SU dynamically fills the spectrum holes. First, the cognitive radio transmitter must confine the spectral content of the transmitter within the selected band(s), i.e., spectrum holes. In other words, its out-of-band interference must be minimized. Second, the receiver should be able to avoid the in-band interference from the other signals on the channel [12]. In other words, in order to increase bandwidth efficiency, receivers need to have acceptable out-of-band rejection capabilities. An OFDM signal has large side lobes and therefore, does not satisfy the first requirement. Moreover, OFDM/FFT does not satisfy the FCC’s envisioned out-of-band rejection requirements [13]. On the other hand, filterbank multicarrier can overcome the spectral leakage problems of OFDM at the
5 transmitter side and therefore lead to less interference from SUs to PUs and other SUs. Filterbank receiver is also capable of providing high out-of-band attenuation. Therefore, filterbank multicarrier has been suggested as an alternative to OFDM. Filterbank multicarrier transmitter and receiver are implemented using a synthesis filterbank at the transmitter and analysis filterbank at the reciver [12]. A block diagram of a transmitter and a receiver filterbank multicarrier is presented in Fig. 1.1. Three classes of filterbank multicarrier (FBMC) have been studied in the literature. Interestingly, the first multicarrier methods that were developed, prior to OFDM, were filterbank-based. The first proposal came from Chang in the 1960s, [14], who presented the conditions required for signaling a parallel set of pulse amplitude modulated (PAM) symbol sequences through a bank of overlapping vestigial side-band (VSB) modulated filters. A year later, Saltzberg extended the idea and showed how the Chang’s method could be modified for transmission of quadrature amplitude modulated (QAM) symbols [15]. Saltzberg showed that a perfect reconstruction FBMC system can be implemented using a half-symbol space delay between the in-phase and the quadrature components of QAM symbols and by proper transmit and receive pulse-shapes in a multichannel QAM system while having the maximum spectral efficiency. In 1980s, Hirosaki progressed more on FBMC and proposed an efficient polyphase implementation for the Saltzberg method [16]–[18]. The method proposed by Saltzberg is referred to as OFDM based on offset QAM or OFDM-OQAM. The offset comes from the half symbol shift between the in-phase and quadrature of each QAM symbol with respect to each other. We refer to this method as staggered modulated multitone (SMT), where the word staggered refers to the fact that the in-phase and quadrature components in each QAM symbols are time staggered. The pioneering work of Chang [14] has received much less (direct) attention than SMT. Nevertheless, the cosine modulated filterbanks that have been widely studied within the signal processing community [19] are nothing but a reinvention of Chang’s filterbank, formulated in discrete time. The use of cosine modulated
6 ej2πf0 n s0 [k] s1 [k]
↑N
hT [n]
e ↑N
hT [n]
⊗
j2πf1 n
⊗
!
ej2πfN −1 n sN −1 [k]
↑N
hT [n]
⊗
Re{}
ej2πfc n
⊗ e−j2πf0 n
⊗
hR [n]
N↓
⊗
hR [n]
N↓
e−j2πf1 n Channel
⊗ e−j2πfc n
Channel
sˆ0 [k] sˆ1 [k]
e−j2πfN −1 n
⊗
hR [n]
N↓
sˆN −1 [k]
Figure 1.1: General structure for filterbank system.
filterbanks for data transmission was widely studied in the 1990s. The advancements in digital subscriber line (DSL) technology led to more work on two classes of FBMC communication systems, namely, filtered multitone (FMT) and discrete wavelet multitone (DWMT) modulation [20]. More recently, in [21] it has been shown that DWMT is essentially using cosine-modulated filterbanks. Therefore, DWMT was renamed to cosine-modulated multitone (CMT). It is also known that CMT is using vestigial sideband (VSB) modulation to transmit PAM symbols [21]. FMT is another multicarrier communication scheme which has been proposed for DSL applications [22]. In FMT, the adjacent subcarriers do not overlap. There-
7 fore, FMT is not bandwidth efficient when it is compared with SMT and CMT.
1.2
OFDM/FFT
OFDM has a number of problems for cognitive radio applications [8]. Herein, we discuss some shortcomings of OFDM in cognitive radio networks. Assuming that the data symbols are independent, the power spectral density (PSD) of an OFDM signal can be described as a summation of PSDs of each subcarrier φ( f ) =
X
φi (f )
(1.1)
i
where φi (f ), the PSD of the i the subcarrier, is given by φi (f ) = Ksinc2 ((f − fi )TS ).
(1.2)
In (1.2), K is the signal level, TS is the period of an OFDM symbol which is the summation of one FFT block and the guard interval, and fi is the center frequency of the ith subcarrier. The sidelobes of the sinc pulse shape is relatively large. Therefore, the outof-band energy generated by an OFDM signal is significant. In a cognitive radio setting, where it is critical to have minimal interference with the PUs, the side-lobes may cause an unacceptable level of interference to the PUs. The sinc shape of the subcarrier PSD is a direct result of the abrupt transition among successive OFDM symbols. The sinc pulse shape can be avoided if we use soft transitions among successive symbols. Cyclic extension of each OFDM symbol from TS to (1 + 2β)TS and windowing by a raised cosine shape can provide us the soft transition. Fig. 1.2 shows how the successive extended OFDM symbols are overlapped. This scheme increases the effective duration of each OFDM symbol from TS to (1 + β)TS , which results in bandwidth loss of
1 . β+1
Weiss et al. investigated different choices of β , and concluded that β as large as 1 is needed to obtain a reasonable suppression of the out-of-band energy [8]. Fig. 1.3 depicts an example of the PSD for various choices of β. This clearly shows the large side-lobes of the rectangular window (β = 0) and how the side-lobes decrease in magnitude as β increases.
8
βTS
TS
βTS
Figure 1.2: Transmit windowing.
One point worth noting is that even though the raised cosine window reduces the side-lobes of the subcarrier spectra, the side-lobes close to the main lobe are still large (Fig. 1.3). This point is noted in [8] where a subcarrier deactivation mechanism is used to avoid subcarrier bands near the active PU band. This mechanism results in a further loss in bandwidth efficiency. Brandes et al. [23], [24] propose a method for side-lobes reduction by using non-zero value deactivated subcarriers. It has been reported in [24] that side-lobes at around −60 dB can be achieved for β = 0.2. Unfortunately, the subcarrier cancelation procedure involves a constraint optimization for each OFDM symbol, which is a computationally expensive task. Using filterbank solution, on the other hand, lower side-lobes (−90 dB) can be achieved at virtually no additional computational cost [13]. As was mentioned at the beginning of this chapter, the out of band rejection capability of a cognitive radio receiver is important to minimize the interference received by a SU from PUs and other SUs. This is a major problem in an OFDM receiver if proper considerations are not taken into account [8]. The solution to the potentially weak out-of-band rejection has been studied in the DSL literature [25]. It is shown that this problem can be solved by applying a window to the received signal prior to passing it to the FFT block for demodulation. Fig. 1.4 presents the method of receiver windowing. If the window is rectangular, one picks N samples of a received OFDM symbol, during the time T . These N samples are then passed to an N -point FFT for demodulation. Using windowing at the receiver, (1 + α)N samples are picked from the OFDM symbol and the window is applied to them. A Fourier transform is then applied to (1 + α)N time-domain samples and the
9 0
β=0 β=0.25 β=0.5 β=1
−10 −20
Φi(f), dB
−30 −40 −50 −60 −70 −80 −90 −100
0
2
4
fT
6
8
10
Figure 1.3: The power spectral density of a subcarrier of an OFDM signal when different choices of the roll-off parameter β is used.
output is decimated to obtain N output samples. This can be realized by aliasing the time-domain samples and applying an N -point FFT to the aliased samples. The arrows in Fig. 1.4 show that the samples in the shaded areas are added to the windowed samples at the two corners of the time period T . While windowing at the receiver provides us with better out-of-band rejection, it requires the addition of cyclic prefix and suffix samples which results in reduction of the bandwidth efficiency of OFDM. Therefore, OFDM can be applied to cognitive radios only if windowing is performed at both transmitter and receiver sides. In addition, deactivation of subcarriers next to other PUs or SUs or applying side-lobe canceler subcarriers is needed. All of the interference suppression methods come at the cost of loss in bandwidth efficiency.
1.3
Filterbank Spectrum Sensing
Most practical sensing methods are based on energy detection, i.e., if the received energy for a given carrier is greater than a defined threshold, that carrier is assumed to be busy. In order to reliably detect available spectrum holes, the channel
10
T
αT 2
(1 + β)TS
αT 2
Figure 1.4: Receiver windowing for OFDM signal.
sensing mechanism needs to have a high spectral dynamic range. While FFT has been suggested as one channel sensing method, note that it suffers from a number of shortcomings that originate from the large side lobes of the frequency response of the filters that characterize each subcarrier. These sidelobes produce spectrum leakage from neighboring subcarriers, resulting in significant inaccuracy and low dynamic range. Thus, with the FFT, SUs are less spectrum agile and cannot detect low power users. This might not be an important issue in systems where the channel access is performed in Time Division Duplex (TDD) and Time Division Multiple Access (TDMA). However, for the systems that incorporate frequency division multiple access and frequency division duplex (FDMA/FDD), the limitations of FFT are serious. Hence, in our solution [12], we propose using filterbanks as the sensing method. By using a filterbank sensing system, the side lobes of the filters associated with each carrier can be made arbitrarily small by adjusting filter length and design. As a result, filters are no longer the limiting factor in achieving high spectral dynamic range. The signal power of the output of the filterbank is then used to estimate the signal spectrum. A block diagram of a filterbank spectrum sensor is presented in Fig. 1.5 where h(t) is the prototype filter used for spectrum sensing, fc + f0 , fc + f1 ,...,fc + fN −1 are the center frequency of the spectrum bands we are sensing.
11
e−j2πf0 t
⊗
h(t)
Energy Detector
⊗
h(t)
Energy Detector
h(t)
Energy Detector
e−j2πf1 t Channel
⊗ e−j2πfc n
e−j2πfN −1 t
⊗
Figure 1.5: General structure of filterbank sensing.
Haykin showed that the multitapper method (MTM) is a near optimal channel sensing method [11]. Unfortunately MTM comes at the expense of high computational complexity. In [12] and [26], it was shown that a filterbank of prolate filters can be used for sensing with virtually ideal performance. In the filterbank sensing method, a prototype filter is designed and then modulated to sense each subcarrier. Implementation can be performed efficiently through a polyphase structure which is described in Chapter 3. It is worth noting that filterbank sensing can be applied to other applications when we need to detect the presence of a signal in a medium. For example, in wiring applications, test signal should be transmitted on unoccupied portions of the spectrum, so that it does not interfere with the live signal on the wire. Therefore, spectrum sensing can be used to detect the presence of the live signal on the wire and to find the spectrum holes which can be used to transmit a test signal [27]. Moreover, one can use the filterbank to generate test pulse shapes that have low out-of-band interference to transmit on the spectrum holes on the wire.
12
1.4
Filterbank Communications
In this section, we give a brief overview of the three filterbank multicarrier communication schemes in the literature: FMT, CMT and SMT. A description of these methods and their applicability to cognitive radios is presented in [12]. 1.4.1
Staggered Modulated Multitone, SMT
Offset quadrature amplitude modulation (OQAM) is a variation of quadrature amplitude modulation (QAM). Saltzberg showed that by choosing a root-Nyquist filter with symmetric impulse response for pulse-shaping at the transmitter and using the same for match filtering at the receiver in a multichannel QAM system, and by introducing a half symbol space delay between the in-phase and quadrature components of QAM symbols, it is possible to achieve baud-rate spacing between adjacent subcarrier channels and still recover the information symbols, free of intersymbol interference (ISI) and intercarrier interference (ICI). This method has an advantage over the conventional orthogonal frequency division multiplexing (OFDM). Unlike OFDM, OQAM multicarrier does not need any cyclic prefix samples for resolving ISI and ICI. OQAM multicarrier, thus, is more bandwidth efficient than the conventional OFDM. As noted earlier, in this dissertation we use the terminology ”Staggered Modulation Multitone (SMT)” to refer to FBMC system that uses OQAM modulation. 1.4.2
Cosine Modulated Multitone, CMT
In a CMT multicarrier system, parallel streams of real data symbols are transmitted using a set of vestigial side-band (VSB) subcarrier channels. Each carrier conveys a stream of pulse amplitude modulated (PAM) symbols. This scheme also has the maximum possible bandwidth efficiency. In a CMT system, in order to transmit N complex symbols on each multicarrier symbol, a system with 2N subcarrier is implemented where each carrier conveys a real symbol, while, in an SMT system the transceiver would have N subcarriers that convey N complex symbols. If SMT symbols are transmitted at the rate of 1/T complex symbols on each subcarrier with a bandwidth of 1/T , an equivalent CMT system with the
13 same data rate, would have a rate of 1/T real symbols on each subcarrier with the bandwidth of 1/2T . Therefore, the same bandwidth is divided into twice as many subcarriers in case of CMT to achieve the same data rate. 1.4.3
Filtered Multitone, FMT
In FMT, subcarriers are arranged such that adjacent subbands do not overlap. As such, FMT may be seen as a multicarrier communication technique that follows the principle of the legacy frequency division multiplexing (FDM) methodology to separate a high-rate data stream into a number of disjoint frequency bands. However, we note that in order to keep the subcarrier bands nonoverlapping, excess bandwidth has to be reserved to allow for a transition band for each subcarrier. Hence, there is some bandwidth loss due to the guardbands in FMT communication systems. 1.4.4
A Comparison of FMT, SMT and CMT
In SMT, each subcarrier band is double side-band modulated and carries a sequence of QAM (i.e., complex-valued) symbols.
Opposed to this, in CMT,
subcarrier modulation is vestigial sideband and the subcarriers carry a sequence of PAM (i.e., real-valued) symbols. Therefore, assuming identical symbol duration and number of subcarriers, the CMT signal occupies half the bandwidth of SMT, and of course, only providing half of its data rate. FMT, on the other hand, introduces guard bands between adjacent subcarriers which are complex modulated. The width of the guardbands depends on the specific system implementation. Therefore, for an identical number of subcarriers and identical symbol timing, FMT requires more bandwidth than SMT [12]. This relationship is shown in Figure. 1.6.
1.5
Contribution of the Dissertation
This dissertation is focused on applications of filterbank multicarrier techniques for cognitive systems. Filterbank multicarrier is applied to cognitive radios for data communications and spectrum sensing. The implementation issues for filterbank techniques are investigated. Data aided methods are proposed for timing and carrier
14 R=
2π T
(a) FMT f R=
NR(1+a), a >0
2π T
(b ) O F DM - OQ A M f R/2=
π
NR
T
( c) C MT f NR/2
Figure 1.6: Comparison between CMT, FMT and SMT
recovery in SMT and CMT communication systems which were proposed for other applications as well. SMT has been a candidate for physical layer of IEEE 802.11 and Third Generation Partnership Project (3GPP) LTE (Long Term Evolution). Furthermore, a cognitive system is designed for fault detection on the live wires based on filterbank multicarrier spectrum sensing and test signal generation. The contribution of this dissertation are as follows: • We study the polyphase implementation structures for SMT and CMT. We show that some of the polyphase structures which are proposed for SMT in the literature are not applicable to frequency selective channels. • Sensitivity of SMT and CMT communication systems with respect to timing phase and carrier offset is investigated. We show that when carrier and timing offset are small, it is possible to use decision directed methods.
15 • Efficient polyphase structure for FBMC are investigated for implementation in wireless channels. A novel formulation for a family of polyphase structures which was used for SMT is derived. Using our derivation, it is shown that some of the SMT analysis structures in [28] and [29] are not applicable to frequency selective channels. • Much of the recent CFO estimation literature attempts to develop blind CFO estimators based on correlation properties of SMT signal. These methods need a large window of signal to detect the carrier and timing offset and are complex to implement. We focus on design of a preamble for packetized communication system which may be used in practical systems. We present a preamble structure which can be used in a packet based communication system to detect the packet, adjust the AGC gain, and perform carrier acquisition, timing recovery, and channel estimation. • While preamble is used for initial timing and carrier offset estimation, without any carrier tracking loop, the carrier and timing phase may drift over the length of the payload. The necessity of carrier and timing offset tracking arises especially due to the use of long packets in data communication systems such as 802.11n. We propose decision directed timing and carrier tracking mechanisms for SMT and CMT. These tracking methods may be used to track the residual offsets after the initial acquisitions are done. • An implementation of a cognitive radio modem is done on a software defined radio platform. The cognitive radios use the filterbank sensing technique which provides us with a reliable spectrum sensor. Therefore, we are able to show that the cognitive radio can detect the presence of low power as well as high power users and transmit in white space. Moreover, the radio can move to an unoccupied part of a band when a PU starts transmitting on the spectrum band that it is currently using.
16 • A cognitive live wire testing system is developed based on filterbank techniques. Analysis filterbank are used for spectrum sensing on a live wire and find spectrum holes to transmit a test signal. Using synthesis filterbanks, test pulse shapes are generated which have very low out-of-band interference and thus do not interfere with live signal. The pulse shape is transmitted on the wire and the reflections are passed through the analysis filterbank; the output of the analysis filterbank is then used to detect faults on the wire.
1.6
Organization of the Dissertation
Filterbank communication methods are presented in Chapter 2. SMT and CMT methods are discussed in details and the orthogonality conditions are derived. Synchronization in multicarrier systems is more essential than single carrier systems due to loss of orthogonality among subcarrier when carrier and timing offset exists. Chapter 2 also discusses the sensitivity of SMT and CMT to carrier and timing offset. It is shown in the simulation results that SMT and CMT outperform OFDM in terms of CFO immunity especially when high SNRs are required. This chapter provides the basic framework for Chapters 3, 4, and 5 where implementation and synchronization issues for SMT and CMT are discussed. Efficient techniques for implementation of filterbanks are presented in Chapter 3. Generic structures for polyphase implementation of synthesis and analysis filterbanks are discussed. The generic polyphase structures are then used to investigate three types of polyphase implementation that has been proposed for SMT in the literature. A novel formulation for the third polyphase structure of SMT is presented. The new derivation shows that third polyphase structure for SMT is not suitable for frequency selective channels. Polyphase structure for CMT systems is also discussed in Chapter 3. Packetized data are vastly used in wireless data communications. Preamble is used at the beginning of a packet to perform automatic gain control (AGC), timing and carrier offset synchronization, and channel estimation. In Chapter 4, we present a novel preamble design for FBMC systems which can be used to perform all the
17 aforementioned tasks. The proposed preamble in Chapter 4 is designed based on a structure similar to IEEE 802.11a and g, and IEEE 802.11n training which are based for OFDM. Our preamble starts with a training field which is called short preamble. The short preamble is followed by a second training which is named long preamble. Short preamble is used for AGC adjustment and course carrier acquisition. A long preamble is appended to the packet which is used by the receiver to perform a more accurate tuning of the carrier frequency, timing phase acquisition, and frequency domain channel estimation. A literature survey on CFO estimation algorithm for SMT based on conjugate and the unconjugated cyclostationary properties of the received signal is presented in Chapter 5. It is noted that the derived estimators in the literature are computationally expensive and require a large number of SMT symbols and in some cases, these methods are designed for a nondispersive channel. In Chapter 5, we propose timing and carrier tracking algorithms for SMT and CMT which are suitable for implementation. The proposed timing and carrier tracking methods are decision directed. A PLL is designed to avoid any built up phase error. Timing phase is tracked by minimizing a cost function. The performance of the proposed methods are studied through computer simulations. Filterbank spectrum sensing is implemented as part of a cognitive radio realization which is discussed in Chapter 6. Lyrtechs Small Form Factor Software Defined Radio was used to implement a cognitive radio solution. In Chapter 6, implementation results are presented which demonstrate that filterbank sensing has high spectral dynamic range and thus can reliably detect the presence of primary and secondary users in the band. The radio can reliably find white spaces in the spectrum and transmit over them. Furthermore, it is shown that the radio can move to an unoccupied part of the spectrum when an interferer start transmitting on the band that it is currently using. This work has received recognition with the best paper award in the 2007 Software Defined Radio Technical Conference and Exhibitions. The cognitive radio network was successfully demonstrated in the 2007 SDR Exhibitions and 2008 IEEE DySpan Demonstration track where we were
18 able to show that our cognitive radios can coexist with other active primary and cognitive devices. Multicarrier reflectometry (MCR) for locating faults on live wires has recently been proposed. Chapter 7 studies the use of filterbanks for generation/synthesis of MCR test signals and also for signal analysis for fault identification and location. We note that the test signals have to be confined to the portion(s) of the frequency band that is (are) free of signals already on the wires in order to avoid interfering with them. Moreover, for effective analysis of the reflected waves, optimal filters that separate the test signal tones and also minimize leakage from the existing signals on the wires should be designed. We discuss the criteria necessary to design effective MCR systems and develop the relevant filterbank design procedures. In Chapter 7, the novel idea of cognitive live wire testing is presented. A cognitive live wire tester measures the live wire signal activities and decides which part of the spectrum should be used for testing. Finally, Chapter 8 presents the concluding remarks and future research. Various areas of research about FBMC are presented in Chapter 8. We discuss that FBMC can be applied to different communication applications and therefore research work is required in many areas for FBMC systems to function in different usage scenarios.
CHAPTER 2
A REVIEW ON FILTERBANK MULTICARRIER COMMUNICATION SYSTEMS Wireless channels are characterized by multipath fading. As a result, intersymbol interference (ISI) happens in wireless channels. ISI is often mitigated through channel equalizers. However, channel equalizers are complex to implement and difficult to adapt in practice when one has to deal with fast-varying wireless channels. Furthermore, an equalizer enhances the noise over the portions of the frequency band where the channel gain is small. Hence, significant degradation might happen as a result of noise enhancement. The spread of ISI across data symbols is proportional to the delay spread of the channel and is inversely proportional to the symbol period. Therefore, by increasing the symbol period equivalent to decreasing the data rate, one can decrease the ISI. In multicarrier communication systems, a data stream is multiplexed into N parallel substreams each of which has a rate N times slower. Therefore, the effect of ISI is reduced proportional to N . The parallel streams are modulated at N subcarriers and added together at the transmitter. The receiver separates the N streams of symbols and demultiplexes them to the original higher rate stream of symbols. OFDM technique is the most used multicarrier technique. It is implemented over a variety of communication products. FBMC are alternative methods for OFDM systems. OFDM systems use guard interval (the cyclic prefix - CP) to combat channel distortion. FBMC, on the other hand, mitigates the problem of channel distortion through filtering techniques. Using proper filter design, adjacent subcarriers are the only ones that overlap, and thus there is almost no interference from nonadjacent subcarriers. As a result, FBMC techniques are better suited
20 for system with high mobility and high doppler effect where orthogonality among subcarriers might be destroyed and ICI mounts to considerable distortion in OFDM signals. Furthermore, as discussed in Chapter 1, for asynchronous multicarrier communications in multiuser systems and cognitive radio networks, big sidelobes of OFDM subcarriers result in interference and therefore loss of performance. There would be up to 50% loss in bandwidth efficiency to suppress the sidelobes [9]. It is also interesting to note that the researchers who have studied filterbanks have invented a class of filterbanks which are called modified DFT (MDFT) filterbank, [30]. Careful study of MDFT reveals that this is in fact a reinvention of Saltzberg’s filterbank (in discrete-time) with emphasis on compression/coding. The literature on MDFT begins with the pioneering works of Fliege, [31], and later has been extended by others, e.g., [32] and [33]. Here, we present an overview fn filterbank communication techniques: SMT, CMT and FMT. We focus more on SMT and CMT where the bandwidth efficiency is maximum. The polyphase implementation of SMT and CMT are discussed. Formulation of FBMC sensitivity to timing and carrier offset is presented in Section 3.
2.1
Filterbank Multicarrier Communications
In this section, we give an overview of the three filterbank multicarrier communication schemes in the literature: FMT, CMT and SMT. We derive the received signal of CMT and SMT techniques and present the filter conditions satisfying near perfect reconstruction. 2.1.1
Staggered Modulated Multitone, SMT
In an SMT transmission system, shown in Fig. 2.1, N parallel complex data streams are passed to N subcarrier transmission filters. The in-phase and quadrature components are then staggered in time by half a symbol period, T /2. The outputs of these filters are modulated using N subcarrier modulators whose carrier frequencies are 1/T -spaced apart. Suppose that we have complex input symbols according to
21
Transmitter h(t)
jsQ 0 (t)
h(t − T /2)
sI1 (t)
2π
π
!{}
Channel
ej2πfc t
π j(N −1)( 2π T t+ 2 )
e
!
h(t)
⊗
Σ
h(t − T /2)
sIM −1 (t) jsQ N −1 (t)
ej ( T t+ 2 ) !
h(t)
!
jsQ 1 (t)
!
sI0 (t)
!
h(t − T /2)
Receiver
e−j ( T t+ 2 ) ! 2π
Channel
⊗ e−j2πfc t
!{}
h(t)
sˆI0 (n)
!{}
h(t + T /2)
sˆQ 0 (n)
!{}
h(t)
!{}
h(t + T /2)
!{}
h(t)
!{}
h(t + T /2)
π
e−j(N−1)( T !
2π
t+ π2 )
sˆI1 (n) sˆQ 1 (n)
sˆIN −1 (n) sˆQ N −1 (n)
Figure 2.1: Structure of the continuous-time SMT system.
sk [n] = sIk [n] + jsQ k [n]
(2.1)
where sIk [n] and sQ k [n] are the real and imaginary parts of the nth symbol of the kth subcarrier, respectively. Let us define sIk (t) and sQ k (t) as sIk (t) =
X
sIk [n]δ(t − nT )
(2.2)
sQ k [n]δ(t − nT ).
(2.3)
n
sQ k (t) =
X n
where δ(t) is the Dirac delta function. The complex-valued baseband SMT modulated signal is defined as
22
x(t) =
N −1 X
xm (t)
(2.4)
jm( 2πt + π ) T 2 . sIm [l]h(t − lT ) + jsQ m [l]h(t − lT − T /2) e
(2.5)
m=0
where xm (t) =
∞ X l=−∞
Analogously, assuming an ideal channel, the output of the receiver, sˆk [n], consists of the real and imaginary components sˆIk [n] and sˆQ k [n], sˆk [n] = sˆIk [n] + jˆ sQ k [n].
(2.6)
In (2.6), sˆIk [n] is found as the real part of the signal at the output of the corresponding matched filter with response h(t) and expressed as sˆIk [n] =
