Adaptive interference suppression algorithms for rcdl500/Sheng Li PhD Adaptive interference suppression

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  • Adaptive interference suppression algorithms for DS-UWB systems

    This thesis is submitted in partial fulfilment of the requirements for Doctor of Philosophy (Ph.D.)

    Sheng Li Communications Research Group

    Department of Electronics University of York

    October 2010

  • ABSTRACT

    In multiuser ultra-wideband (UWB) systems, a large number of multipath components (MPCs) are introduced by the channel. One of the main challenges for the receiver is to effectively suppress the interference with affordable complexity. In this thesis, we focus on the linear adaptive interference suppression algorithms for the direct-sequence ultra- wideband (DS-UWB) systems in both time-domain and frequency-domain.

    In the time-domain, symbol by symbol transmission multiuser DS-UWB systems are considered. We first investigate a generic reduced-rank scheme based on the concept of joint and iterative optimization (JIO) that jointly optimizes a projection vector and a reduced-rank filter by using the minimum mean-squared error (MMSE) criterion. A low-complexity scheme, named Switched Approximations of Adaptive Basis Functions (SAABF), is proposed as a modification of the generic scheme, in which the complexity reduction is achieved by using a multi-branch framework to simplify the structure of the projection vector. Adaptive implementations for the SAABF scheme are developed by using least-mean squares (LMS) and recursive least-squares (RLS) algorithms. We also develop algorithms for selecting the branch number and the model order of the SAABF scheme. Secondly, a novel linear reduced-rank blind adaptive receiver based on JIO and the constrained constant modulus (CCM) design criterion is proposed that offers higher spectrum efficiency. Adaptive implementations for the blind JIO receiver are developed by using the normalized stochastic gradient (NSG) and RLS algorithms. In order to obtain a low-complexity scheme, the columns of the projection matrix with the RLS algorithm are updated individually. Blind channel estimation algorithms for both versions (NSG and RLS) are implemented. Assuming the perfect timing, the JIO receiver only requires the knowledge of the spreading code of the desired user and the received data.

    In the frequency-domain, we propose two adaptive detection schemes based on single- carrier frequency domain equalization (SC-FDE) for the block by block transmission mul- tiuser DS-UWB systems, which are termed structured channel estimation (SCE) and di- rect adaptation (DA). Both schemes use the MMSE linear detection strategy and employ a cyclic prefix. In the SCE scheme, we perform the adaptive channel estimation in the frequency-domain and implement the despreading in the time-domain after the FDE. In

  • this scheme, the MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, we propose low-complexity algorithms for estimating these parameters. In the DA scheme, the interference suppression task is fulfilled with only one adaptive filter in the frequency-domain and a new signal expression is adopted to simplify the design of such a filter. LMS, RLS and conjugate gradient (CG) adaptive algorithms are then developed for both schemes.

    Another strand of investigation considers adaptive detectors and frequency domain equalization for multiuser DS-UWB systems with block transmissions and biased esti- mation methods. Biased estimation techniques can provide performance improvements to the existing unbiased estimation algorithms. In this work, biased adaptive estimation techniques based on shrinkage estimators are devised and incorporated into RLS-type al- gorithms. For the SCE scheme, automatic shrinkage factor mechanisms are proposed and incorporated into RLS estimators, obtaining a lower MSE of the channel estimation. For the DA scheme, the automatic shrinkage factors are incorporated directly to the adaptive receiver weights. The results show that a shorter data support is required by the proposed biased DA-RLS technique. An analysis of fundamental estimation limits of the proposed frequency domain biased estimators is included along with the derivation of appropriate Cramér-Rao lower bounds (CRLB).

  • CONTENTS

    Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

    Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

    Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

    List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

    List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

    List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

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

    1.1 UWB Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

    1.1.1 UWB Pulse-Shaping . . . . . . . . . . . . . . . . . . . . . . . . 2

    1.1.2 Spread-Spectrum Techniques in UWB . . . . . . . . . . . . . . . 4

    1.1.3 UWB Modulation . . . . . . . . . . . . . . . . . . . . . . . . . 5

    1.1.4 UWB Channel Model . . . . . . . . . . . . . . . . . . . . . . . 6

    1.2 Adaptive Filtering and Estimation Algorithms . . . . . . . . . . . . . . . 7

    1.2.1 The Least-Mean Square Algorithm . . . . . . . . . . . . . . . . 8

    1.2.2 The Recursive Least-Squares Algorithm . . . . . . . . . . . . . . 9

    1.2.3 Conjugate Gradient Algorithm . . . . . . . . . . . . . . . . . . . 10

  • 1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

    1.3.1 Motivation for Time-Domain Signal Processing . . . . . . . . . . 11

    1.3.2 Motivation for Frequency-Domain Signal Processing . . . . . . . 13

    1.4 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

    1.5 List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

    2. DS-UWB System and Signal Models . . . . . . . . . . . . . . . . . . . . . . 19

    2.1 Time-Domain System and Signal Model . . . . . . . . . . . . . . . . . . 19

    2.2 Frequency-Domain System and Signal Model . . . . . . . . . . . . . . . 22

    3. Reduced-rank Interference Suppression Schemes Based on Joint and Iterative Op- timization and Switching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

    3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

    3.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

    3.3 Generic Reduced-Rank Scheme and Problem Statement . . . . . . . . . . 27

    3.4 Proposed SAABF Scheme and Filter Design . . . . . . . . . . . . . . . . 29

    3.4.1 Discrete Parameter Optimization . . . . . . . . . . . . . . . . . . 31

    3.4.2 Filter Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

    3.5 Adaptive Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

    3.5.1 The LMS Version . . . . . . . . . . . . . . . . . . . . . . . . . . 33

    3.5.2 The RLS Version . . . . . . . . . . . . . . . . . . . . . . . . . . 35

    3.5.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . 36

  • 3.6 Model Order and Parameter Adaptation . . . . . . . . . . . . . . . . . . 37

    3.6.1 Branch Number Selection . . . . . . . . . . . . . . . . . . . . . 38

    3.6.2 Rank Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . 38

    3.6.3 Inner Function Length Selection . . . . . . . . . . . . . . . . . . 39

    3.7 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

    3.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

    4. Blind Reduced-rank Adaptive Receivers for DS-UWB Systems Based on the JIO and CCM Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

    4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

    4.2 Proposed Blind JIO Reduced-Rank Receiver Design . . . . . . . . . . . . 50

    4.2.1 Blind JIO Reduced-Rank Receiver . . . . . . . . . . . . . . . . . 50

    4.2.2 Blind Channel Estimation . . . . . . . . . . . . . . . . . . . . . 52

    4.3 Proposed JIO-NSG Algorithms . . . . . . . . . . . . . . . . . . . . . . . 54

    4.3.1 JIO-NSG Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 55

    4.3.2 Blind Channel Estimator for the NSG Version . . . . . . . . . . . 58

    4.4 Proposed JIO-RLS Algorithms . . . . . . . . . . . . . . . . . . . . . . . 58

    4.4.1 JIO-RLS Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 60

    4.4.2 Blind Channel Estimator for the RLS version . . . . . . . . . . . 63

    4.5 Complexity Analysis and Rank Adaptation Algorithm . . . . . . . . . . . 64

    4.5.1 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . 64

  • 4.5.2 Rank Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . 66

    4.6 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

    4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

    5. Frequency Domain Adaptive Detectors for SC-FDE in Multiuser DS-UWB Sys- tems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

    5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

    5.2 Proposed Linear MMSE Detection Schemes . . . . . . . . . . . . . . . . 74

    5.2.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . 75

    5.2.2 Det