使用廣義空間調變技術之巨量天線多輸入多輸出系統是最近被發展出來的一種傳輸架構，並且可被視為空間調變技術與空間多工技術的結合。空間調變技術能藉由每次傳送時只開啟一根傳輸天線來完全避免掉通道間干擾，且解碼偵測器也相較於其他系統較於簡單，然而，經由一些研究顯示若使用廣義空間調變技術可以更進一步有效的提升頻譜效率。
基於這些研究，我們此篇論文的研究目的是改善先前與空間調變技術相關的研究並加入廣義空間調變技術來做比較，我們先利用壓縮感測技術來做多輸入多輸出系統通道的估計，更重要的是我們改善先前被提出的使用空間調變技術來進行稀疏通道盲估計之研究，我們新增了一些參數並改變通道追蹤的機制，最後儘管我們的系統複雜跟運算度比較高，但因為我們考慮了廣義空間調變技術使我們的系統較為彈性，且模擬結果也顯示我們的方法得到較好的位元誤差率。

Multiple-input multiple-output (MIMO) system with generalized spatial modulation (GSM) is a recently developed transmission scheme and can be regarded as spatial modulation combined with spatial multiplexing. Spatial modulation (SM) systems entirely avoid the inter-channel interference (ICI) due to only one active antenna at each instance and the corresponding detector is relatively simple compared with other MIMO systems. However, some researches have shown that the GSM systems can achieve higher spectral efficiency by sending multiple independent symbol-streams with multiple active transmit antennas. Based on these researches, our goal in this study is to improve the previous works and further compare the performances between SM and GSM with different detectors. Moreover, we apply compressed sensing (CS) technique to do channel estimation, instead of conventional channel estimation (CCE) of MIMO systems which expend much time and may be a worse way in some fading environments. Most important of all, we improve the blind CS-based channel estimation (Blind CS-Based CE) by modifying the mechanism of channel tracking as our proposed method, which is referred as CS-based fast blind channel tracking. Even though our proposed method is more complicated than blind CS-based CE, our system model is more flexible and the results show that our proposed method can achieve better bit error rate (BER) performance.

[1] L. Zheng and D. N. C. Tse, “Diversity and multiplexing: A fundamental tradeoff in multiple-antenna channels,” IEEE Trans. Inf. Theory, vol. 49, no. 5, pp. 1073–1096, May 2003.
[2] J. Mietzner, et al., “Multiple-Antenna Techniques for Wireless Communications — A Comprehensive Literature Survey,” IEEE Communications Surveys & Tutorials, vol. 11, pp87-105, 2009.
[3] Larsson, E.G., Edfors, O., Tufvesson, F., et al.: “Massive MIMO for next generation wireless systems,” IEEE Commun. Mag., vol. 52, pp. 186–195, 2014.
[4] R. Mesleh, H. Haas, Y. Lee, and S. Yun, “Interchannel interference avoidance in mimo transmission by exploiting spatial information,” in Proc. PIMRC’05, Sept. 2005.
[5] D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 1289–1306, Apr. 2006.
[6] R. Y. Mesleh, H. Haas, S. Sinanovi’c, C. W. Ahn, and S. Yun, “Spatial modulation,” IEEE Trans. Veh. Technol., vol. 57, no. 4, pp. 2228–2241, July 2008.
[7] A. Younis, N. Serafimovski, R. Mesleh, and H. Haas, “Generalized Spatial Modulation,” Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2010.
[8] J. Jeganathan, A. Ghrayeb, and L. Szczecinski, “Generalized space shift keying modulation for MIMO channels,” in Proc. IEEE Personal, Indoor and Mobile Radio Communications. 2008
[9] E. Basar, U. Aygolu, E. Panayirci, and H. V. Poor, “Space-time block coded spatial modulation,” IEEE Trans. Commun., vol. 59, no. 3, pp. 823–832, Mar. 2011.
[10] R. Mesleh, M. Di Renzo, H. Haas, and P. M. Grant, “Trellis Coded Spatial Modulation,” IEEE Trans. on Wireless Commun., vol. 9, no. 7, pp. 2349–2361, July 2010.
[11] C. Yu et al., “Compressed sensing detector design for space shift keying in MIMO systems,” IEEE Commun. Lett., vol. 16, no. 10, pp. 1556–1559, Oct. 2012.
[12] C.-T. Lin, W.-R. Wu, C.-Y. Liu, “Low-Complexity ML Detectors for Generalized Spatial Modulation Systems,” IEEE Trans. Commun., vol. 63, no. 11, pp. 4214-4230, Nov. 2015.
[13] H.-W. Tsai, Y.-R. Tsai, “A Study of Blind Sparse Channel Estimation Based on Compressed Sensing for Massive MIMO Systems with Spatial Modulation,” July. 2017.
[14] K. Yu and B. Ottersten, “Models for MIMO propagation channels—A review,” J. Wireless Commun. Mob. Comput., vol. 2, no. 7, pp. 653–666, Nov. 2002.
[15] C.-R. Tsai, Y.-H. Liu, and A.-Y. Wu, “Efficient compressive channel estimation for millimeter-wave large-scale antenna systems,” IEEE Trans. Signal Process., vol. 66, no. 9, pp. 2414–2428, 2018.
[16] P. Dent, G. Bottomley and T. Croft, “Jakes fading model revisited,” IEEE Electronics Letters, vol. 39, no. 13, pp.1162-1163, Jun. 1993.
[17] J. A Tropp, A C. Gilbert, and M. 1. S trauss, “Simultaneous sparse approximation via greedy pursuit,” in Proc. ICASSP, 2005.