Luận Văn A Study of Channel Estimation for OFDM Systems and System Capacity for MIMO-OFDM Systems

Chapter 1: Introduction

The research on wireless communication systems with high data rate, high spectrum
efficiency and reliable performance is a hot spot. There are several advanced
communication technologies or protocols proposed recently, including Orthogonal
frequency division multiplexing (OFDM) [1], multiple input multiple output (MIMO) [2],
Ultra-Wideband (UWB) technology [3], cognitive radio [4], World Interoperability for
Microwave Access (WiMAX) [92], and 3GPP Long Term Evolution (LTE) [92], [93].
OFDM is an efficient high data rate transmission technique for wireless communication.
OFDM presents advantages of high spectrum efficiency, simple and efficient
implementation by using the fast Fourier Transform (FFT) and the inverse Fast Fourier
Transform (IFFT), mitigration of inter-symbol interference (ISI) by inserting cyclic prefix
(CP) and robustness to frequency selective fading channel. MIMO is the use of multiple
antennas at both the transmitter and receiver to improve communication performance. It is
one of several forms of smart antenna technology. MIMO technology has attracted
attention in wireless communications, because it increases in data throughput without
additional bandwidth or transmit power. It achieves this by higher spectral efficiency and
link reliability or diversity. The combination of MIMO with OFDM technique is a
promising technique for the next generation wireless communication. A new protocol draft
employing the MIMO-OFDM as the physical layer technology, IEEE 802.11n, as an
amendment to IEEE 802.11 standards has been proposed [53]. Wireless LAN technology

has seen rapid advancements and MIMO-OFDM has gradually been adopted in its
standards. The following table shows the existing IEEE 802.11 WLAN protocols.


Contents
Declarations . i
Acknowledgements . ii
Contents iii
List of Figures . vii
Chapter 1: Introduction 1
1.1 Research motivation 4
1.2 Organization and contributions of the thesis . 5
Chapter 2: OFDM systems and MIMO systems 9
2.1 Wireless Channel . 10
2.1.1 Large scale propagation . 11
2.1.2 Small scale propagation . 13
2.1.3 Typical wireless channel models 17
2.2 OFDM systems . 20
2.2.1 Basic principles and characteristics for OFDM systems . 21
2.2.2 Peak-to-Average (PAR) of OFDM systems . 30
2.2.3 Channel estimation for OFDM systems . 33
2.2.4 Synchronization of OFDM systems . 38
2.2.5 Advantages and disadvantages of OFDM systems 39
2.3 MIMO systems . . 40
2.3.1 Basic MIMO system model . 40
2.3.2 Functions of MIMO systems . . 42
2.3.3 Overview of Space Time codes . . 45
2.3.4 Capacity of MIMO systems . 52
iii
2.4 MIMO-OFDM systems . 54
2.5 Summary . 56
Chapter 3: Channel estimation for OFDM systems over quasi-static fading channels
. 57
3.1 Introduction . 58
3.2 System Model . 61
3.3 The Proposed Fast LMMSE Algorithm 63
3.3.1 Properties of the channel correlation matrix in frequency domain 63
3.3.2 The proposed fast LMMSE channel estimation algorithm 65
3.3.3 Computational complexity comparison between the proposed method and
the conventional LMMSE method 69
3.4 Analysis of the Mean Square Error (MSE) of the Proposed Fast LMMSE
Algorithm 70
3.4.1 MSE analysis of the conventional LMMSE algorithm 71
3.4.2 MSE analysis for the proposed fast LMMSE algorithm 72
3.5 Numerical and Simulation Results . . 75
3.6 Conclusion 81
Chapter 4: Channel estimation and data detection for OFDM systems over fast
fading channels 87
4.1 Introduction . 88
4.2 System Model . 91
4.3 The Proposed Channel Estimation and Data Detection 92
4.3.1. The proposed pilot pattern 92
iv
4.3.2. Channel Estimation and data detection for the first M1 OFDM symbols of
each block . 94
4.3.3. Channel estimation and data detection for the last M2 OFDM symbols of
each block . 95
4.3.4. Summary of the proposed channel estimation and data detection 98
4.4. Analysis of MSE of the proposed channel estimation method 99
4.4.1. MSE analysis of channel estimation for the first M1 OFDM symbols . 100
4.4.2. MSE analysis of channel estimation for the last M2 OFDM symbols 103
4.4.3 MSE analysis of channel estimation for one OFDM block . 105
4.5 Numerical and Simulation Results . . 106
4.6. Conclusion . 112
Chapter 5: MIMO-OFDM system capacity with imperfect feedback channel . 118
5.1 The open-loop and closed-loop capacity for MIMO Systems 119
5.1.1 MIMO system model . 119
5.1.2 MIMO system capacity 120
5.1.3 Numerical Results and discussion 124
5.2 The closed-loop capacity with imperfect feedback channel for MIMO-OFDM
systems 127
5.2.1 System Model 128
5.2.2 Closed-Loop Capacity and Feedback SNR for MIMO-OFDM Systems 130
5.2.3 Numerical Results 136
5.3 Summary . 142
Chapter 6: Capacity of OFDM systems over time and frequency selective fading
v
channels 144
6.1 Introduction . 145
6.2 OFDM System Model . 147
6.3 OFDM System Capacity . 148
6.3.1 OFDM system capacity over Rayleigh fading channels 148
6.3.2 OFDM system capacity over Ricean fading channels . 153
6.4 Numerical and Simulation Results . . 157
6.5 Conclusion 161
Chapter 7: Conclusions and future works 167
7.1 Conclusions . 167
7.2 Future works . 169
APPENDIX A: The derivation of the rank of channel frequency autocorrelation matrix
RHH in Chapter 3 . 170
APPENDIX B: The derivation of equation (3-20) in Chapter 3 . 171
APPENDIX C: The derivation of the joint PDF of two arbitrary correlated Ricean random
variables 173
Appendix D: List of Abbreviations . 176
REFERENCES 179
Publications . 191