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研究生:李景硯
研究生(外文):Jing-Yen Lee
論文名稱:結合基於波束成型選擇方法以及粒子最佳化演算法之空間編碼於大規模多輸入多輸出系統
論文名稱(外文):Beamforming-selection Precoding with ParticleSwarm Optimization in Massive MIMO Systems
指導教授:李枝宏李枝宏引用關係
指導教授(外文):Ju-Hong Lee
口試委員:謝宏昀方文賢陳巽璋劉玉蓀
口試委員(外文):Hung-Yun HsiehWen-Hsien FangShiunn-Jang ChernYu-Sun Liu
口試日期:2016-07-27
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:93
中文關鍵詞:大規模輸入與輸出空間編碼通道資訊(CSI)波束成型分頻雙工(FDD)合作式粒子演算法(CCPSO)位元錯誤率(BER)
外文關鍵詞:multiple input multiple output(MIMO)spatial precodinglimited channel state information (CSI)beamformingfrequency-division duplex (FDD)cooperatively coevolving particle swarm optimization(CCPSO)bit error rate (BER)
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  • 被引用被引用:2
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之前的學者提出了beamforming-selection precoding (BSP)方法以降低frequency-division duplex (FDD) 大規模多輸入多輸出(massive multiple-inputmultiple-output ,massive MIMO)之downlink training以及channel state information(CSI) 回傳的負擔,然而BSP方法有一個缺陷,他們使用了一組他們自行定義的固定beamformnig系數作為BSP方法的precoder,這會造成使用者(UEs)之間的干擾問題,因此這篇論文提出了一種方法修正BSP,我們運用了粒子最佳化演算法(PSO)去搜尋最佳的beamformnig系數運用於BSP,如此便可以大幅地降低使用者間的干擾,我們稱我們提出的方法為BSP-PSO,BSP-PSO比起原本的BSP不僅可以達到更好的位元錯誤率(BER),也保留了原本BSP擁有較低的downlink training 以及 CSI回傳的負擔的優點。此外,我們提出了一種新的基於平均錯誤率公式的PSO適應值方程式,數個模擬結果顯示了在各種通道環境中或考慮mutual coupling情況下,我們的BSP-PSO方法在位元錯誤率(BER)效能下比原來的BSP方法有著非常大的改進。

The beamforming-selection precoding (BSP) has been used by researchers to reduce overheads of the downlink training and the channel state information feedback in the frequency-division duplex (FDD) massive multiple-inputmultiple-output (MIMO) systems. However, the BSP has a weakness that using a set of pre-defined fixed beamforming coefficients can cause the interference problem between user equipments (UEs). Thus, this paper proposes a method that incorporates the BSP with a cooperatively coevolving particle swarm optimization(PSO) algorithm such that the designed beamforming coefficients can greatly reduce the severe interference between UEs. This proposed method, termed the BSP-PSO method, not only can achieve better bit error rate (BER) performance than the original BSP, but also preserves advantages of the BSP having lower overheads of the downlink training and the CSI feedback. Additionally, we propose a new fitness function for this cooperatively coevolving PSO based on the derived average BER formula. Numerical simulations are also demonstrated for both the urban-macro and the urban-micro wireless scenarios to validate the superior BER performance of the proposed precoding method.

致謝 .............................................. I
摘要 ............................................. II
ABSTRACT ........................................ III
目錄 ............................................. IV
圖目錄 ........................................... VI
表目錄 ......................................... VIII
第一章 緒論 ....................................... 1
1.1 研究背景 ...................................... 1
1.2 研究動機....................................... 2
1.3 論文之主要貢獻 ................................ 3
1.4 論文之組織架構 ................................ 4
第二章 MIMO 系統之空間編碼方法 .................... 6
2.1 FDD MIMO 系統架構 ............................. 6
2.2 Beamforming-selection precoding (BSP)空間編碼方法 .......................... 9
2.2.1 The Downlink Training of Beamforming-selection precoding (BSP)........... 9
2.2.2 The precoding scheme and feedback scheme of beamforming-selection precoding(BSP) ... 12
2.3 Block diagonal Scheme with Quantized Feedback(BD-QF)空間編碼方法..... 14
2.4 BSP 方法及BD-QF 方法之比較 ............. 15
2.4.1 計算複雜度比較 ....................... 15
2.4.2 CSI overhead 比較 .................... 17
第三章 粒子最佳化演算法 .................... 20
3.1 Patical Swarm Optimization (PSO) ........20
3.2 Cooperatively Coevolving Particle Swarm Optimization (CCPSO)................ 22
第四章 LINE OF SLIGHT 通道情況下之BSP 方法的SINR 分析及改善 .............. 26
4.1 BSP 方法之平均SINR ..................... 27
4.2 模擬結果與分析 ......................... 28
第五章 多路徑通道情況下之BSP 方法的SINR 分析及改善 ............. 31
5.1 Channel model A ........................... 32
5.2 考慮Mutual Coupling 對於BSP 的影響 ........ 32
5.3 模擬結果及分析 ............................ 34
第六章 多路徑通道情況下之BSP 方法的BER 分析及改善 .............. 36
6.1 BSP 方法的MMSE MIMO receiver .............. 36
6.2 Spaitail Correlation MIMO model ........... 38
6.3 PSO 的適應值方程式(fitness function) ...... 40
6.3.1 平均實部SINR 方法 ....................... 40
6.3.2 平均與最小實部SINR 方法 ................. 41
6.3.3 理論平均錯誤率方法 ...................... 41
6.4 多路徑通道情況下之BSP 方法的BER 模擬與分析 ................... 43
6.4.1 使用空間相關性之MIMO 通道矩陣模型 ....... 43
6.4.2 使用MIMO 通道矩陣模型 A ................. 49
6.4.3 結合空間相關性之MIMO 通道矩陣模型與Mutual Coupling ........... 51
6.4.4 結合空間相關性之MIMO 通道矩陣模型與通道矩陣模型A以及Mutual Coupling ....................... 53
第七章 考慮3GPP 通道模型與大規模輸入與輸出(Massive MIMO)情況下之BSP方法的BER 分析及改善 ................. 56
7.1 Urban macro 情況下BSP 方法的BER 模擬與分析 .... 57
7.2 Urban micro 情況下BSP 方法的BER 模擬與分析 .... 63
第八章 加入正交頻分多址(OFDM)之BSP 方法的位元錯誤率分析 ...... 69
8.1 多輸入與輸出之正交頻分多址 (MIMO-OFDM) ... 69
8.2 加入正交頻分多址 (OFDM)之BSP 方法的位元錯誤率模擬與分析 ......... 72
8.2.1 在urban-macro 情況下加入OFDM 之BSP 方法的BER 模擬與分析 .... 72
8.2.2 在urban-micro 情況下加入OFDM 之BSP 方法的BER 模擬與分析 .... 77
第九章 總結與未來方向 .............. 82
附錄(APPENDIX) ..................... 83
參考文獻 ........................... 91

[1] M.-F. Tang, M.-Y. Lee, and B. Su, “Beamforming-based spatial precoding in FDD massive MIMO systems,” in Proc. IEEE 48th Systems and Computers Asilomar Conference, Pacific Grove, CA, USA, vol. 1, Nov. 2014, pp. 2073–2077.
[2] D. Love and R. Heath, “Limited feedback unitary precoding for spatial multiplexing systems,” IEEE Transactions on Information Theory, vol. 51, no. 8, pp. 2967–2976, Aug. 2005.
[3] R. Kudo, S. Armour, J. McGeehan, and M. Mizoguchi, “A channel state information feedback method for massive MIMO-OFDM,” Journal of Communications and Networks, vol. 15, no. 4, pp. 352–361, Aug. 2013.
[4] Q. Spencer, A. Swindlehurst, and M. Haardt, “Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels,”IEEE Transactions on Signal Processing, vol. 52, no. 2, pp. 461–471, Feb.2004.
[5] Q. Spencer, C. Peel, A. Swindlehurst, and M. Haardt, “An introduction to the multi-user MIMO downlink,” IEEE Communications Magazine, vol. 42, no. 10, pp. 60–67, Oct. 2004.
[6] T. Marzetta, “Noncooperative Cellur Wireless with Unlimited Numbers of Base Station Antennas,” IEEE Transactions on Wireless Communications, vol. 9, no. 11, pp. 3590 – 3600, November 2010.
[7] M.M.Khodier and C. Christodoulou, “A Linear Array Geometry Synthesis With Minimum Sidelobe Level and Null Control Using Particle Swarm optimization,” IEEE Transactions on Antennas and Propagation, vol. 53, no. 8, pp. 2674–2679, Aug 2005.
[8] J. Kennedy and R. Eberhart, “particle swarm optimization,” in Proceedings of IEEE International Conference on Neural Network, Perth,Australia, vol. 4, Nov.-Dec. 1995, pp. 1942–1948.
[9] A. H. Mehana and A. Nosratinia, “Diversity of MMSE MIMO
Receivers,” IEEE Transactions on Information Theory, vol. 58, no. 11, pp. 6788–6805, November 2012.
[10] X. Li and Y. Yao, “Cooperatively coevolving particle swarms for large scale optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 2, pp. 210 – 224, April 2012.
[11] “Spatial channel model for multiple input multiple output (MIMO) simulations (3GPP TR25.996 version 6.1.0),” Sep. 2003.
[12] J. Salo, G. D. Galdo, J. Salmi, P. Kysti, M. Milojevic,
D. Laselva, and C. Schneider, “MATLAB implementation of the
3GPP spatial channel model (3GPP TR25.996 version 6.1.0)
available:http://radio.aalto.fi/en/research/rf applications in mobile communications/propagation research/matlab scm implementation,”Jan. 2005.
[13] C. Balanis, Antenna Theory Analysis and Design. New York: Wiley, 1997.
[14] S.Durrani and M.E.Bialkowski, “Effect of mutual coupling on the interference rejection capabilities of linear and circular arrays in CDMA systems,” IEEE Trans.on Antennas and Propagation, vol. 52, no. 4, pp.427–447, April 2004.
[15] D. Tse and P. Viswanath, Fundamentals of Wireless Communication. The Edinburgh Building, Cambridge, UK: Cambridge University Press, 2005.
[16] J. Robinson and Y. Rahmat-Samii. “ Particle swarm optimization in electromagnetics”, IEEE Transactions on Antennas and Propagation,vol 52,no.2,pp.397-407,Feb.2004
[17] J.-H. Lee and C.-C. Cheng,“SPATIAL CORRELATION OF MULTIPLE ANTENNA ARRAYS IN WIRELESS COMMUNICATION SYSTEMS”, Progress In Electromagnetics Research, Vol. 132, 347(368, 2012)
[18] Leslie Hogben,“Handbook of Linear Algebra, Second Edition”, London:Chapman and Hall/CRC; 2 edition (November 26, 2013)
[19] Dianne P. O''Leary,“Scientific Computing with Case Studies”, Philadelphia: Society for Industrial Mathematics (December 19, 2008)
[20]李受益,Research on Performance of Wireless Communication Systems Using Adaptive Arrays,國立台灣大學電信工程研究所碩士論文,2009
[21] P. P. Vaidyanathan, See-May Phoong and Yuan-Pei Lin, “Signal Processing and Optimization for Transceiver Systems”, The Edinburgh Building, UK: Cambridge University Press (April 2010)

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