面對龐大的無線傳輸資料量，單輸入單輸出(Single Input Single Output)的天線系統已經無法滿足需求，因而發展多輸入多輸出(Multiple Input Multiple Output)的系統；然而在追求高頻譜效益(spectral efficiency)的同時，卻無法兼顧能源效益(energy efficiency)。近年來，許多研究嘗試改善這項缺點，尋求一個折衷的版本，因此衍生出了空間調變(spatial modulation)的技術。此篇論文中，我們在廣義空間鍵移多輸入多輸出(generalized space shift keying)系統下提出了偵測器的設計。有別於傳統多輸入多輸出的偵測器設計，我們是針對空間調變多輸入多輸出系統下設計偵測器，而正交匹配追蹤(orthogonal matching pursuit)這項技術，因為低複雜度的優勢而被廣泛使用在稀疏信號(sparse signal)的還原；然而在各通道之間相似較高的情況，或是mutual coherence較大時，往往會因為天線之間的相關性太高而使得偵測器混淆選到錯誤的天線，還原出錯誤的信號。也因此許多研究注重在額外設計等化器(equalizer)來降低通道之間的相似度，進一步提升傳統正交匹配追蹤的性能，卻往往付出了極大的複雜度成本。相反的，我們所提出的偵測器則是有辦法在一個比先前的研究中用更低的複雜度達到更佳的表現，並且能夠透過每個階段保留多的候選者(candidate)作為參數上的調整，針對不同的系統需求改變還原表現，甚至逼近最大概似估計(maximum likelihood)的結果。最後透過各演算法完整的複雜度比較以及錯誤率來顯現出我們提出偵測器的優勢。

To deal with the rapid growth of wireless data traffic, we propose the detector design in generalized space shift keying (GSSK)-MIMO systems. With the advantage of low complexity, orthogonal matching pursuit (OMP) is widely adopted. Nevertheless, failure may occur in the identification of antenna index under a large mutual coherence of the channel. Thus, lots of studies and research have been conducted to further improve the performance of standard OMP. Most of the previous works focus on minimizing mutual coherence and designing additional equalizer at the receiver, which leads to high complexity in the end. On the other hand, the algorithm we propose achieves better performance than those from the previous works with lower complexity. Meanwhile, the proposed algorithm is adjustable by simply increasing the amount of candidates in each iteration, depending on the requirements of the systems. Consequently, the proposed detector is capable of achieving the same performance as maximum likelihood. Simulation results and comprehensive survey of the complexity are given to demonstrate the superior performance among other methods.

1 Introduction 1
1.1 MIMO and SM-MIMO Systems . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 MIMO Systems 7
2.1 MIMO System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Spatial Modulation MIMO System Model . . . . . . . . . . . . . . . . . 9
2.3 GSM-MIMO System Model . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 GSSK-MIMO System Model . . . . . . . . . . . . . . . . . . . . . . . . . 12
3 Compressive Sensing-Based Orthogonal Matching Pursuit Detectors 15
3.1 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.1.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.1.2 Mutual Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1.3 Compressive Sensing for SM-MIMO System . . . . . . . . . . . . 19
3.2 Conventional OMP Algorithms . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.1 Standard OMP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.2 Optimized OMP I . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.3 Optimized OMP II . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4 Proposed List OMP Detector 31
4.1 List OMP Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Conclusion

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