正交分頻多工系統容易遭受載波頻率偏移的影響不僅對在載波中產生子載波內互干擾以及對偵測訊號產生複數可乘性失真效應。不管是複數可乘性失真或是子載波內互干擾，兩者皆會隨著傳輸區塊的不同而跟著變動並對接收訊號產生未知失真，即使在較小的載波頻率偏移下仍然對系統產生嚴重系統表現的影響，進而使得在接收端執行通道估測及資料偵測顯得更為困難。為了讓通道估測及資料偵測的過程中不受到載波頻率偏移效應的影下，在本論文中我們提出了三種克服載波頻率偏移效應的技術，分別為連鎖式訊號預編碼正交分頻多工系統、退化式哈達碼編碼共軛傳輸機制以及稀疏式訓練序列輔助式正交分頻多工系統。
在連鎖式訊號預編碼正交分頻多工系統中，連鎖式預編碼系連鎖一改良式相關性階層編碼與一退化式哈達碼編碼配合一領航訓練序列式資料傳輸結構所組成，以助接收端在存有載波頻率偏移效應的通道下進行聯合多路徑通道及固定複數可乘性失真估測、時變性固定複數可乘性失真估測及補償以及子載波內互干擾壓抑等效用。實驗模擬顯示在存有載波頻率偏移以及多路徑通道的環境下，相較於過往的訊號預編碼技術連鎖式訊號預編碼正交分頻多工系統能提供更佳的系統效能。此外，為了能在共軛消除技術中提供與連鎖式訊號預編碼正交分頻多工系統相同之時變性複數可乘性失真壓抑效能，藉由結合退化式哈達碼編碼以及共軛傳輸機制的想法，我們提出了一退化式哈達碼編碼共軛傳輸機制可同時進行複數可乘性失真效應之估測與補償以及子載波內互干擾壓抑之效果。實驗模擬顯示我們所提出的機制在多路徑衰落通道以及載波頻率偏移存在下，相較於過往的共軛消除技術能提供更好的系統效能，另外在損失一半資料傳輸效能下退化式哈達碼編碼共軛傳輸機制亦可表現優於連鎖式訊號預編碼正交分頻多工系統。
為了能在大載波頻率偏移量的環境下仍然能提供有效的載波頻率偏移壓抑效能，我們提出一稀疏式訓練序列輔助式正交分頻多工系統以提供接受端能夠在進行通道估測以及資料偵測的同時不受到大載波頻率偏移量的影響。在稀疏式訓練序列的輔助下，一個近似最大相似度載波頻率偏移效應及通道響應估測被提出用來從接收訊號中擷取出載波頻率偏移效應急通道響應資訊，進而在執行通道估測及資料偵測的同時幫助作載波頻率偏移效應之補償。實驗模擬顯示提出的稀疏式訓練序列輔助式正交分頻多工系統能提供與完美已知載波頻率偏移效應急通道響應的狀況下相似的系統效能，並在大載波頻率偏移量下表現優於訊號預編碼技術。

Orthogonal frequency-division multiplexing (OFDM) is highly sensitive to carrier frequency offset (CFO), which not only causes intercarrier interference (ICI) among subcarriers but also introduces complex multiplicative distortion (CMD) to all detected subcarrier symbols. Due to unknown CFO, both ICI and CMD are time-variant, thus complicating channel estimation and data detection at the receiver. In order to perform channel estimation and data detection without being affected by CFO effect, the concatenated precoded OFDM, reduced Hadamard-coded conjugate cancellation (RHCC) approach, and sparse-training-sequence-aided OFDM systems are proposed in this thesis to enable CFO effect mitigation.
In the concatenated precoded OFDM system, a concatenated precoder which is constructed by concatenating an outer modified correlative precoder with an inner reduced Hada-mard precoder is developed in conjunction with a training-prefixed data frame structure to process data symbols prior to OFDM modulation and enable joint estimation on channel multipath and constant CMD, time-variant CMD estimation and compensation, and ICI suppression at the receiver in the presence of CFO. Simulation results show that the concatenated precoded OFDM system provides much better error performance than conventional signal coding approaches in the presence of CFO and multipath fading. In order to suppress the time-variant CMD effect in conjugate cancellation (CC)-based approaches, a reduced Hadamard-coded conjugate cancellation (RHCC) approach is proposed by combining the ideas of reduced Hadamard precoding and conjugate transmission, and also enables CMD estimation and compensation, and ICI self-cancellation at the receiver in the presence of CFO. Simulation results show that the RHCC provides much better error performance than conventional CC-based approaches in the presence of CFO and multipath fading, and performs better than the concatenated precoded OFDM system at the cost of halving data throughput.
In order to provide effective CFO effect suppression for a wide range of CFO values, a sparse-training-sequence-aided OFDM system is proposed to enable channel estimation and data detection without being affected by CFO effect especially for large CFO values. With the aid of the sparse training sequence, an approximate maximum likelihood estimation on CFO effect and channel response is developed to retrieve the CFO information from the received signal, and then facilitate the corresponding CFO effect compensation during channel estimation and data detection. Simulation results show that the sparse-training-sequence-aided OFDM system provides similar error performance to that with perfectly known CFO and channel response, and performs better than signal precoding approaches for large CFO values.

Contents
Abstract i
Contents iii
List of Figures vi
List of Tables x
Abbreviations xi
Notations xiii
1 Introduction 1
1.1 Signal Precoding Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Thesis Motivation and Contributions . . . . . . . . . . . . . . . . . . 3
1.2 Training Sequence-Based Approaches . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Thesis Motivation and Contributions . . . . . . . . . . . . . . . . . . 6
2 Concatenated Precoded OFDM System 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Signal and System Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 TCMD Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 TCMD Estimation Under Flat Fading . . . . . . . . . . . . . . . . . 14
2.3.2 TCMD Estimation Under Multipath Fading . . . . . . . . . . . . . . 17
2.4 ICI Suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4.1 ICI Suppression Under Flat Fading . . . . . . . . . . . . . . . . . . 19
2.4.2 ICI Suppression Under Multipath Fading . . . . . . . . . . . . . . . 21
2.4.3 Outer Decoding With TCMD Compensation . . . . . . . . . . . . . 21
2.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.5.1 ABER Characteristics of Concatenated Precoded OFDM . . . . . . . 24
2.5.2 Comparison With Conventional Signal Coding Approaches . . . . . . 25
2.5.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3 Reduced Hadamard-Coded Conjugate Transmission in OFDM System 32
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3 The CFO Effect Mitigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3.1 TCMD Estimation and Compensation . . . . . . . . . . . . . . . . . 37
3.3.2 CC Decoding and ICI Suppression . . . . . . . . . . . . . . . . . . . 38
3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.1 ABER Characteristics of RHCC System . . . . . . . . . . . . . . . . 39
3.4.2 Comparison With Conventional Signal Precoding Systems . . . . . . 40
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4 Sparse-Training-Sequence-Aided OFDM System 44
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Design of Estimation of h and E . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.1 ML Estimation of h and E . . . . . . . . . . . . . . . . . . . . . . . 49
4.3.2 AML Estimation of E . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.4 LS-Based TCMD Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.5.1 Optimal Sparsity Depth for STS-Based Approach . . . . . . . . . . . 56
4.5.2 Performance of STS-Based Approach . . . . . . . . . . . . . . . . . 57
4.5.3 Comparison With FTS-Based Approach . . . . . . . . . . . . . . . . 61
4.5.4 Comparison With Signal Precoding Approaches . . . . . . . . . . . . 62
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5 Conclusion 64
Bibliography 66
Appendix A: Derivation of vtm Im fUg vk 71
Appendix B: Derivation of (2.13) 72
Appendix C: Proof that Q(k) is skew-symmetric 76
Appendix D: Derivation of (2.19) 77
List of Publications 78

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