NOVEL RECEIVER ARCHITECTURES FOR MOBILE COMMUNICATIONS ANG WEE PENG (B.Eng. (EE), M.Sc. (EE), NUS) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2003 Acknowledgments I would like to thank my supervisors, A/Prof H.K. Garg and A/Prof Farhang B. Boroujeny for their guidance and patience throughout my candidature. I would also like to thank A/Prof Garg for introducing me to the exciting area of research on turbo codes, and for his comments and suggestions throughout the course of this research program. On a personal note, I would like to dedicate this thesis to my wife Lai Pheng for her support and love throughout this period. i Contents Acknowledgments i Summary vii List of Tables ix List of Figures x List of Acronyms xvi Introduction 1.1 Evolution Of Mobile Cellular Communications . . . . . . . . . . . . 1.1.1 First Generation (1G) Mobile Communications . . . . . . . 1.1.3 Third Generation Mobile Communications . . . . . . . . . . 1.1.2 1.2 1.3 1.1.4 Second Generation (2G) Mobile Communications . . . . . . Beyond Third Generation Mobile Communications . . . . . Mobile Propagation Channel Impairments & Optimum Receivers 15 19 For Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.3.1 Channel Modelling . . . . . . . . . . . . . . . . . . . . . . . 25 1.3.3 Linear Prediction . . . . . . . . . . . . . . . . . . . . . . . . Applications of Adaptive Signal Processing in Communications . . . 1.3.2 1.3.4 Channel Equalization . . . . . . . . . . . . . . . . . . . . . . Interference Cancellation . . . . . . . . . . . . . . . . . . . . ii 25 27 29 31 1.4 Aim of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 1.6 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.5 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . Review of Variable Step-Size Least Mean Square Algorithms 2.1 2.2 2.3 2.4 2.5 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Problem Formulation of Tracking a Non- 37 37 stationary Environment for Adaptive Filters . . . . . . . . . . . . . 39 Non-stationary Environment . . . . . . . . . . . . . . . . . . . . . . 41 Analysis of Mean Square Error of LMS Algorithm in Tracking a Derivation of Optimum Step-Size Parameters . . . . . . . . . . . . . 2.4.1 Optimum Step-Size Parameters For Multiple Step-Size LMS 2.4.2 Optimum Step-Size Parameter For Common Step-Size LMS 45 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Existing Variable Step-Size LMS Algorithms . . . . . . . . . . . . . 48 2.4.3 Stability Conditions . . . . . . . . . . . . . . . . . . . . . . 47 2.5.1 Classi¯cation and Naming Convention of the VSLMS Algo- 2.5.2 Common Step-Size VSLMS Algorithm - Mathews' Algorithm 50 2.5.3 2.5.4 2.5.5 2.5.6 2.5.7 2.6 33 2.5.8 rithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benveniste's Algorithm - Another Common Step-Size VSLMS 50 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Benveniste's Multiple Step-Size VSLMS Algorithm . . . . . 54 Mathews' Multiple Step-Size VSLMS Algorithm . . . . . . . Multiplicative Update Versus Linear Update Of Step-Size 53 Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Sign Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 55 Normalization of Adaptation Parameter ½ . . . . . . . . . . Performance of Common Step-Size VSLMS Algorithms . . . . . . . iii 55 56 2.6.1 2.6.2 2.7 2.6.3 56 rithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Mathews' Vs Benveniste's Common Step-Size VSLMS Algo- Performance Under High And Low SNR . . . . . . . . . . . Performance of Multiple Step-Size VSLMS Algorithms In Tracking 61 2.7.2 62 2.7.1 Non-stationary Channel Model . . . . . . . . . . . . . . . . 2.7.3 Multiple Step-Size Vs Common Step-Size . . . . . . . . . . . Simulation Set-up . . . . . . . . . . . . . . . . . . . . . . . . Mathews' Vs Benveniste's Multiple Step-Size VSLMS Algo- rithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A New Class of Variable Step-Size LMS Algorithms 3.1 3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A New Class of VSLMS Algorithms . . . . . . . . . . . . . . . . . . 3.2.1 Benveniste's Multiple Step-Size Algorithm . . . . . . . . . . 3.2.3 Gradient Filtering View of the Proposed and Benveniste's 3.2.2 3.3 3.4 3.5 58 A Multipath Channel . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7.4 2.8 Simulation Set-up . . . . . . . . . . . . . . . . . . . . . . . . A New Class of Multiple Step-Size VSLMS Algorithm . . . . 61 63 65 67 69 69 71 71 72 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Classi¯cation of the Algorithms and Computational Complexity . . 74 3.2.4 A New Class of Common Step-Size VSLMS Algorithm . . . Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Tracking a Time-Varying Plant Using Proposed c-VSLMS 3.4.2 Tracking Performance In Multipath Channel Using Proposed 73 74 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 m-VSLMS Algorithms . . . . . . . . . . . . . . . . . . . . . 77 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv 87 Turbo Codes Fundamentals 4.1 4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 4.2.1 Encoding Convolutional Codes . . . . . . . . . . . . . . . . . 90 Representation of Convolutional Codes : State Diagram and 91 The Turbo Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.2.3 Recursive Systematic Convolutional (RSC) Codes . . . . . . 92 The Turbo Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 4.4.1 Maximum A Posteriori (MAP) Algorithm . . . . . . . . . . 4.4.3 Max-Log-MAP Algorithm . . . . . . . . . . . . . . . . . . . 103 4.4.4 4.4.5 4.4.6 4.4.7 4.6 90 Trellis Diagram . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 4.5 88 Convolutional Codes . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 4.3 88 4.4.8 98 Log-MAP Algorithm . . . . . . . . . . . . . . . . . . . . . . 101 Requirements For Turbo Decoding In Rayleigh Fading Chan- nels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Simulation Set-up . . . . . . . . . . . . . . . . . . . . . . . . 105 Performance Of Turbo Decoder In AWGN . . . . . . . . . . 106 Sensitivity Of Turbo Decoder To Errors in Eb =No . . . . . . 106 Performance Of Turbo Decoder In Rayleigh Fading Channels 108 Other Related Coding Schemes . . . . . . . . . . . . . . . . . . . . 110 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Channel Estimation For Turbo Decoding Over Rayleigh Fading Channels 5.1 5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5.2.1 Transmitter Model . . . . . . . . . . . . . . . . . . . . . . . 117 5.2.3 Receiver Model With Iterative Channel Estimation . . . . . 119 5.2.2 5.3 113 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . 118 Proposed Channel Estimation Filters . . . . . . . . . . . . . . . . . 125 v 5.3.1 Fixed Characteristics Channel Estimation Filters . . . . . . 126 5.3.3 Comparison of Computational Complexity of Channel Esti- 5.3.2 5.4 5.5 5.6 Selection of Filter Parameters . . . . . . . . . . . . . . . . . . . . . 138 5.5.1 5.5.2 5.9 Parameters For Fixed Characteristics Filter . . . . . . . . . 138 Parameters For Variable Step-Size LMS Filter . . . . . . . . 142 Performance in Stationary Rayleigh Fading Channel . . . . . . . . . 144 5.6.1 Slow Normalized Fading Rate, fd Ts = 0:005 . . . . . . . . . 144 5.6.3 Convergence of Mean Square Error of Channel Estimates . . 153 5.6.4 5.8 mation Filters . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Simulation Set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 5.6.2 5.7 Adaptive Channel Estimation Filter . . . . . . . . . . . . . . 129 5.6.5 Fast Normalized Fading Rate, fd Ts = 0:02 . . . . . . . . . . 151 Soft Decision Feedback Versus Hard Decision Feedback . . . 158 Other Feedback Schemes . . . . . . . . . . . . . . . . . . . . 165 Performance in Non-stationary Rayleigh Fading Channel . . . . . . 168 Performance of Proposed c-VSLMS-III-M Algorithm Versus Exist- ing c-VSLMS-M Algorithms . . . . . . . . . . . . . . . . . . . . . . 173 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Conclusions 177 6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 6.2 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 References 183 vi Summary In digital communications, especially for mobile communications, adaptive signal processing techniques are widely used to improve the system performance. For example, adaptive equalization is needed to combat a multipath propagation channel that is usually unknown a-priori. Furthermore, the propagation channel can also be time varying as the mobile station transceiver is not static. Thus, the adaptive equalization algorithms not only need to converge quickly to a steady state dur- ing a short training stage, they also need to track the changes of the propagation channel to maintain an acceptable performance. The major contributions of this thesis are as follows. 1. New Class of Variable Step-size Least Mean Square Algorithms. The ¯rst contribution of this thesis is the development of a new class of variable stepsize least mean square (VSLMS) algorithms that shows fast convergence and good tracking properties in a non-stationary environment. This new class of VSLMS algorithms is shown to be suitable for channel estimation that is essential for good performance in a mobile propagation environment. It is demonstrated that the algorithms are superior compared to existing algorithms in performance and not require a signi¯cant increase in complexity. 2. New Iterative Channel Estimation Receiver Architecture. The second contribution resulting from this research is the development of a new receiver architecture incorporating iterative channel estimation. This receiver architecture vii leads to a signi¯cant improvement in coding gain over one that does not perform iterative channel estimation, for turbo decoding over fading channels. This pro- posed receiver architecture uses time multiplexed pilot symbols for initial channel estimation which is further improved by feeding back only the detected message (systematic) bits after each decoding iteration. We also demonstrate that our re- ceiver architecture achieves performance that is same as that of an existing one that uses both message and parity bits for improving the channel estimates, but with reduced complexity. 3. New Fixed Characteristics Channel Estimation Filters. The third contribution is the establishment of the suitability of di®erent channel estima- tion ¯lters and their parameters for good performance under known, unknown and non-stationary fading rates. This has not been thoroughly investigated in the existing literature. The FIR (¯nite impulse response) and the DFT (discrete Fourier transform) ¯lters are shown to be suitable under both slow and fast fading when the fading rates are known. For good performance, we found that the cut-o® frequency of the channel estimation ¯lters needs to be greater than the normalized fading rate (about 1.1 to 1.5 times) as opposed to being equal to the normalized fading rate as proposed previously. As for the equal weight moving average ¯lter, it is suitable only under slow fading when the fading rate is known. 4. Adaptive Channel Estimation Filter. The fourth contribution is the successful application of the new class of VSLMS algorithms developed here as an appropriate channel estimation ¯lter in the proposed receiver architecture. This VSLMS ¯lter does not assume a-priori knowledge of the fading rate. When the fading rate changes, the VSLMS ¯lter is also able to track the channel well, achieving performance better than that of the equal weight moving average and the FIR ¯lters. The equal weight moving average and FIR ¯lters have ¯xed characteristics and are hence unable to respond to a channel that has a time varying fading rate. viii List of Tables 3.1 Classi¯cation of VSLMS Algorithms and Complexity . . . . . . . . 5.1 Complexity of Channel Estimation Filters . . . . . . . . . . . . . . 136 ix 75 gain. This is similar to the result reported in [68], where both parity and message bits are fed back to the channel estimator after each decoding iteration. The channel estimates are found to converge faster than the BER. They also converge faster at lower fading rate. Hence, the channel coe±cients need not be re-estimated at every decoding iterations. Soft decision feedback does not always provide large improvement in performance. Noticeable gain in performance using soft decision feedback is seen only when the detected message bits to be fed back are not reliable during the ¯rst few iterations. The equal weight moving average ¯lter, the FIR ¯lter, and the DFT ¯lter are suitable when the fading rate is known a-priori. At very slow normalized fading rate, e.g. fd Ts = 0:005, all three ¯lters with characteristics selected based on the known fading rate, achieve BER performance close to the optimum Wiener ¯lter. Thus, the simpler equal weight moving average ¯lter is good enough under very slow fading. When the normalized fading rate is much faster, e.g. fd Ts = 0:02, the equal weight moving average ¯lter no longer performs close to the Wiener ¯lter, while the FIR and DFT ¯lters still do. When the fading rate is unknown a-priori, the c-VSLMS-III-M ¯lter is able to perform close to the optimum Wiener ¯lter and has equal or better performance than the other three ¯lters that assume a- priori knowledge of the fading rate. This is valid under both slow and fast fading rates. Under a non-stationary Rayleigh fading channel, the c-VSLMS-III-M ¯lter is also shown to perform better than the equal weight moving average and the FIR ¯lters. Finally, the proposed c-VSLMS-III-M ¯lter also demonstrates superior performance compared to existing c-VSLMS-I-M ¯lter presented in Chapter and 3. 176 Chapter Conclusions 6.1 Summary There are two major phases in this research. The ¯rst phase of this research focuses on the tracking properties of the Variable Step-Size Least Mean Square (VSLMS) algorithm in a non-stationary environment. A literature survey of the past and present works in that area is conducted to compare the strength and weaknesses of the various forms of the VSLMS algorithm and also to understand the theoretical techniques to analyze their tracking behavior. Further research in this area has resulted in a new class of VSLMS algorithms that achieves better tracking performance than the existing algorithms. The reasons for their superior performance as compared to existing algorithms are also presented. In addition, extensive computer simulations of the new class of algorithms together with the existing algorithms in tracking time-varying multipath communications channel are conducted. The results con¯rm the superior tracking performance of the new class of VSLMS algorithms. These results are reported at the Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000 [74]. A journal paper has also been published in the IEEE Transactions on Signal Processing [65]. As highlighted throughout this thesis, the mobile propagation environment is non177 stationary in nature and is characterized by a-priori unknown and time varying fading rate. For good BER performance, the channel response has to be estimated and tracked as it varies. Hence, the new class of VSLMS algorithms are good candidates in tracking the non-stationary channel. In the second phase of the research, we focus on developing a new receiver architecture incorporating iterative channel estimation suitable for turbo decoding over Rayleigh fading channels. This is motivated by the interest in turbo codes that are capable of providing high coding gain as well as the fact that they are recommended as one of the channel coding schemes in 3G systems. As highlighted in Chapter 5, there are two key components in this new receiver architecture developed here. The ¯rst is the feedback of only the message (systematic) bits after each turbo decoding iteration to the channel estimation ¯lter to improve the initial estimates that are based on pilot symbols. The second is the channel estimation ¯lter which must be matched to the channel's normalized fading rate for maximum rejection of noise. As the channel fading rate is unknown a-priori and also time varying, the proposed VSLMS algorithms in Chapter which possess good tracking properties in a non-stationary environment are applied here. Our research shows that the proposed receiver achieves signi¯cant improvement in coding gain, comparable to that achieved by the technique where both message and parity bits are fed back to the channel estimator [68]. We also present a detailed study of three additional channel estimation ¯lters that are suitable in the iterative channel estimation process. We establish the impact of the proposed ¯lter parameters on BER under a variety of fading conditions that have not been thoroughly investigated before. This allows one to choose the appropriate channel estimation ¯lter and its parameters to achieve good BER performance. The equal weight moving average ¯lter, the FIR ¯lter, and the DFT ¯lter are suitable when the fading rate is known a-priori. At very slow normalized fading rate, e.g. fd Ts = 0:005, all three ¯lters with characteristics selected based on the known fading rate, achieve BER performance close to the optimum Wiener ¯lter. Thus, the simpler 178 equal weight moving average ¯lter is good enough under very slow fading. When the normalized fading rate is much faster, e.g. fd Ts = 0:02, the equal weight moving average ¯lter no longer performs close to the Wiener ¯lter, while the FIR and DFT ¯lters still do. These results were published in two conference papers [75]-[76]. However, these ¯xed characteristic ¯lters assume knowledge of the channel's fading rate. Hence, they are not practical in a mobile propagation environment where the channel characteristics such as the fading rate, are unknown a-priori and are likely to be time-varying. We then demonstrate that our new class of variable step-size LMS (VSLMS) ¯lter developed in Chapter can be applied successfully in the channel estimation problem, achieving BER performance close to that obtained by the optimum Wiener ¯lter. In addition, it does not assume a-priori knowledge of the channel's characteristics. Furthermore, it is able to perform well in a non- stationary environment as opposed to conventional non-adaptive ¯lters. These results were presented in a conference paper [77] and also submitted to IEEE Transactions in Wireless Communications for publication [78]. 179 6.2 Publications The followings are the publications made during the period of research: 1. W.P. Ang, and B. Farhang Boroujeny, `Gradient Adaptive Step-Size LMS Al- gorithms: Past Results and New Developments', Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000 (AS-SPCC 2000), pp. 278-282, Oct. 2000. 2. W.P. Ang, and B. Farhang Boroujeny, `A New Class of Gradient Adaptive Step-Size LMS Algorithms', IEEE Trans. on Signal Processing, vol.49, No. 4, pp.805-810, Apr. 2001. 3. W.P. Ang, and H.K. Garg, `A New Iterative Channel Estimator For Turbo Decoding Over Flat Fading Channel', International Conference on Information, Communications & Signal Processing (ICICS 2001), Oct. 2001. 4. W.P. Ang, and H.K. Garg, `A New Iterative Channel Estimator For The LogMAP and Max-Log-MAP Turbo Decoder in Rayleigh Fading Channel', Global Telecommunications Conference, 2001 (GLOBECOM '01), IEEE, Vol.6, pp. 3252 -3256, Nov. 2001. 5. W.P. Ang, and H.K. Garg, `A New Adaptive Channel Estimator For Turbo Decoding In Rayleigh Fading Channels', 8th IEEE International Conference on Communication Systems (ICCS 2002), 26-28 Nov 2002. 6. W.P. Ang, and H.K. Garg, `Iterative Channel Estimators For Turbo Decod- ing in Rayleigh Fading Channels', submitted to IEEE Transactions in Wireless Communications, submitted Jun 2002, revised and submitted in Mar 2003. 180 7. W.P. Ang, and H.K. Garg, `Decision Feedback Schemes For Enhanced Channel Estimation For Turbo Decoding In Rayleigh Fading Channels', accepted for publication at the International Conference on Information, Communications & Signal Processing (ICICS 2003). 6.3 Future Work In our proposed receiver architecture which re¯nes the channel estimate by feeding back the detected message (systematic) bits to the channel estimator, the channel estimates are re¯ned after each turbo decoding iteration. As the performance of the turbo code is known to converge after a certain number of iterations, further work can be done to establish suitable criteria to stop re¯ning the channel estimates. With such criteria found, the computational cost of re-estimation of the channel response can be reduced. In this thesis, we only focus on estimating a single Rayleigh fading channel for turbo decoding. In 3G systems where the turbo codes are recommended, multiple Rayleigh fading paths will be encountered. Further research can be done to extend the proposed receiver architecture and the corresponding VSLMS channel estima- tion ¯lter to enhance the system performance in such a situation. Various schemes of combining the multipaths in a turbo-coded WCDMA system in 3G could be investigated to identify the best solution. Recently, another area of active research is the use of Orthogonal Frequency Division Multiplexing, OFDM, (an e±cient form of multicarrier transmission tech- nology) for very high data rate transmission. Here, the modulation scheme includes QAM besides BPSK and QPSK. Already OFDM is used in IEEE 802.11a wireless local area network (WLAN) standard providing data rate of up to 54 Mbps. It is also recommended as the radio transmission technology for 4G systems. Further research can be done to develop suitable channel estimation algorithms for OFDM employing QAM modulation schemes. 181 In this thesis, we have used a new class of VSLMS ¯lter developed in our research to achieve adaptivity in the channel estimation process. Other approaches also exist and one such scheme is proposed by [79]. However, it is based on pilot tones. Hence, it needs to be adapted for the current application of iterative channel estimation for turbo decoding using pilot symbols. 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Technology, vol. 40, no. 3, pp. 532- 545, Aug. 1991. 191 [...]... 2G 3G 4G AMPS First Generation Mobile Communications Second Generation Mobile Communications Third Generation Mobile Communications Fourth Generation Mobile Communications Advanced Mobile Phone Service Autoregressive AR Additive White Gaussian Noise AWGN Binary Phase Shift Keying BPSK Bandwidth-Time Product BT Code Division Multiple Access CDMA Discrete Fourier Transform DFT Digital Signal Processor... will ¯rst review the evolution of mobile cel- lular communications highlighting the interest to search for ways to enhance sys- tem performance in a mobile propagation environment and hence improve system throughput This is followed by a discussion of the mobile propagation channel, its e®ect on the transmitted signal and the optimum receiver structure for the fading mobile propagation channel Subsequently,... VSLMS algorithms 56 Simulation set-up for comparing tracking performance of existing Performance of c-VSLMS-I-M vs c-VSLMS-II-M algorithms 59 and low SNR 60 Performance of c-VSLMS-I-M vs c-VSLMS-II-M algorithms for high x 2.5 Simulation set-up for comparing tracking performance of existing 2.6 Performance of c-VSLMS-I-M vs m-VSLMS-I-M algorithms in track-... high performance radio frequency components with low noise ¯gures to improve receiver sensitivity Another area of active research is related to the challenges posed by the impairments caused by the mobile propagation channel The mobile propagation channel causes impairment in the form of signal 1 fading and rapid phase changes These have adverse e®ects on the transmitted signal, especially for coherent... e.g a change in fading rate caused by changes in the mobile user's speed, the channel estimation algorithm must also track the changes without the need to increase the number of pilot symbols Novel receiver architectures capable of achieving good performance by using a minimum number of pilot symbols and adaptive signal processing algorithms, are therefore suitable candidates in the channel estimation... AMPS D-AMPS Digital Enhanced Cordless Telephone DECT EDGE FFT Enhanced Data-Rates for GSM Evolution Fast Fourier Transform Forward Error Correction FEC Frequency Division Multiple Access FDMA Finite Impulse Response FIR xvi Frequency Modulation FM Gaussian Minimum Shift Keying GMSK Global System for Mobile GSM International Mobile Telephony IMT IMT Direct Spread IMT-DS IMT Frequency Time IMT-FT IMT Multi-Carrier... to the system or the mobile phones When the software of the mobile phone requires updating, some of the software feature upgrades can be directly transmitted to the mobile phone without involving the customer All 2G systems also have improved authentication and voice privacy capability This has dramatically reduced the fraudulent use of mobile phones 1.1.3 Third Generation Mobile Communications Work... ½ The simulation parameters are the same as those used in Figure 3.1 79 Performance of proposed c-VSLMS-III-M algorithm compared with c-VSLMS-I-M and c-VSLMS-II-M algorithms for high and low SNR 80 Simulation set-up for comparing tracking performance of proposed and existing m-VSLMS-M algorithms Tracking performance of m-VSLMS algorithms using multiplica- 81 tive update recursion under... used to mitigate the amplitude fading e®ects of large scale path loss and small scale channel fading to improve system performance However, for enhanced performance of the FEC, there is also a need to estimate the chan- nel coe±cients For example, the channel estimates allow the receiver to remove the phase rotations caused by the channel to achieve coherent demodulation of quadrature amplitude modulation...List of Figures 1.1 Evolution of Mobile Cellular Communications 4 1.3 Fade margin versus percent reliability for Rayleigh fading 8 1.2 1.4 1.5 1.6 1.7 1.8 1.9 Concept of Mobile Cellular Communications Signal processing operations in GSM 10 Generalized Viterbi Equalizer . First Generation Mobile Communications 2G Second Generation Mobile Communications 3G Third Generation Mobile Communications 4G Fourth Generation Mobile Communications AMPS Advanced Mobile Phone Service AR. Generation Mobile Communications . . . . . . . . . . 15 1.1.4 Beyond Third Generation Mobile Communications . . . . . 19 1.2 Mobile Propagation Channel Impairments & Optimum Receivers For Fading. NOVEL RECEIVER ARCHITECTURES FOR MOBILE COMMUNICATIONS ANG WEE PENG (B.Eng. (EE), M.Sc. (EE), NUS) A THESIS SUBMITTED FOR THE DEGREE O F DOCTOR OF PHILO SOPHY DEPARTMENT