適用於64x4多使用者多輸出多輸入輸出系統之星座點範圍結合增益控制晶格簡化一位元預編碼處理器

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研究生：	何保葆 Ho, Pao-Pao
論文名稱：	適用於64x4多使用者多輸出多輸入輸出系統之星座點範圍結合增益控制晶格簡化一位元預編碼處理器 Constellation Range and Gain-Controlled Lattice-Reduction-Aided 1-Bit Precoding Processor for 64x4 MU-MIMO System
指導教授：	黃元豪 Huang, Yuan-Hao
口試委員:	陳喬恩 Chen, Chiao-En 蔡佩芸 Tsai, Pei-Yun 蔡尚澕 Tsai, Shang-Ho
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 通訊工程研究所 Communications Engineering
論文出版年：	2019
畢業學年度：	108
語文別：	英文
論文頁數：	73
中文關鍵詞：	多使用者多輸入多輸出系統、量化預編碼、晶格簡化、星座點、系統架構、硬體實現
外文關鍵詞：	MU-MIMO, quantized, precoding, Lattice-reduction-aided, Constellation, VLSI
相關次數：	點閱：3 下載：0
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多使用者多輸入輸出系統與大規模陣列天線是能為下一世代行動通訊提供高吞吐量與資料量的重要技術。為了減少因大量天線的高解析度數位類比轉換器所造成的高成本和高功耗，量化預編碼藉由降低數位類比轉換器的解析度以達到能量能被限制在可忍受的範圍。為了解決現有演算法皆需要多次的遞迴以達到收斂，本篇論文針對一位元預編碼處理器提出星座點範圍結合增益控制晶格簡化演算法，利用星座點範圍設計應用在高調變訊號且使得接收端不需要額外處理所接收的訊號，結合傳統多輸入輸出搜尋方式與晶格簡化以降低複雜度。本論文所提出的預編碼演算法使用TSMC 40nm CMOS製程來實作成硬體。所提出的預編碼處理器適用於 64x4 多使用者多輸入多輸出系統與 64-QAM 傳送訊號。處理器的最高頻率為 153 MHz，功耗為 230 mW，最大資料吞吐量能達到 4.16 M 訊號/每秒。

Massive multi-user (MU) multiple-input and multiple-output (MIMO) systems becomes a crucial technique for the next generation communication systems due to the high throughput and reliability. With the increasing antennas equipped in the base station, power consumption turns into the high-priority challenge. Each antenna is connected to a pair of high resolution digital to analog converters (DAC) which resulting high power consumption. Thus, quantized precoding was proposed to utilize the low-resolution DAC for keeping power in a tolerable constraint. The quantized function brings about the nonlinear distortion which is hard to compensate. In this thesis, we propose a constellation range and gain-controlled lattice-reduction-aided algorithm for 1-bit precoding. Simulation results show that the proposed algorithm gains robust performance in high-QAM signaling as well as reduces the computation complexity by 60% when frame size equals to 100. We also design and implement an 1-bit precoding processor based on proposed algorithm. This chip is implemented by using TSMC 40nm COMS process technology. The processor supports the 64x4 MU-MIMO systems under 64-QAM transmitted symbols. The operating frequency of this chip is 153MHz and power consumption
is 230mW. The throughput of this chip achieve 4.16M symbols per second.

 Introduction       1
1    Quantized Precoding for Massive Multi-user MIMO Systems   1
2    Research Motivation       3
3    Organization of This Thesis     4
4    Notations       4
 Quantized Precoding and Lattice Reduction            7
1    System Model of Quantized Precoding for Massive Multi-user MIMO     7
2    Linear Quantized Precoding       9
2.1Linear Quantized Precoding Problem for 1-Bit DACs       11
2.2Linear Wiener Filter Precoding         11
3    Nonlinear Quantized Precoding        12
3.1Nonlinear Quantized Precoding Problem for 1-Bit DAC       12
3.2Nonlinear 1-Bit Sphere Precoding     13
3.3Binconvex 1-Bit Precoding            19
3.4Iterative Discrete Estimation        22
3.5Constellation Range Design for 1-Bit Precoding        25
4 Lattice Reduction           29
4.1LLL Algorithm              30
4.2Joint QR and LR Pre-processing with Enhanced Constant  through-put LLL Algorithm            32
4.3Lattice-Reduction-Aided MIMO Systems           35
 Proposed Nonlinear 1-Bit Precoding Agorithm               39
1    Constellation Range and Gain-Controlled Lattice-Reduction-Aided 1-Bit Precoding        39
2    Simulation Result and Analysis        43
 VLSI Architecture          49
1    System Architecture        49
2    Gain-Controlled Joint QR and LR Pre-processing with ECTLLL Processor   51
3    Successive Interference Cancellation Processor        57
4    Timing Schedule         57
 Implementation Result      61
1    Design Flow      61
2    Layout Hierarchy and Chip I/O     63
3    Chip Specification and Result     63
 Conclusion      69
Reference           71
                                

[1] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up mimo: Opportunities and challenges with very largearrays, ”IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 40–60, 2011.
[2] E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, “Massive mimo for next generation wireless systems, ”IEEE Communications Magazine, vol. 52, no. 2,pp. 186–195, 2014.
[3] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive mimo: Benefits and challenges, ”IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 742–758, 2014.
[4] R. H. Walden, “Analog-to-digital converter survey and analysis ,”IEEE Journal on Selected Areas in Communications, vol. 17, no. 4, pp. 539–550, 1999.
[5] A. K. Saxena, I. Fijalkow, and A. L. Swindlehurst, “On one-bit quantized zf pre-coding for the multiuser massive mimo downlink, ”IEEE Sensor Array and Multi-channel Signal Process, 2016.
[6] O. B. Usman, H. Jedda, A. Mezghani, and J. A. Nossek, “Mmse precoder for massive mimo using 1-bit quantization,” pp. 3381–3385, 2016.
[7] S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein, and C. Studer, “Quantized precoding for massive mu-mimo,” vol. 65, no. 11, pp. 4670–4684, 2017.
[8] S. Jacobsson, G. Durisi, W. Xu, and C. Studer, “Mse-optimal 1-bit precoding for multiuser mimo via branch and bound,” pp. 3589–3468, 2018.
[9] A. C. H. Sarieddeen, M. M. Mansour, and A. Chehab, “On the scalability of non-linear search-based precoding for 1-bit massive mimo systems,” 2018.
[10] J. C. Chen, “Alternating minimization algorithm for one-bit precoding in massive multiuser mimo system, ”IEEE Trans. on Vehicular Technology, vol. 67, no. 8, 2018.
[11] O. Castaneda, T. Goldstein, and C. Studer, “Pokemon: A non-linear beamforming algorithm for 1-bit massive mimo, ”in proc. IEEE Int. Conf. Acoust., Speech, Signal Process, pp. 3464–3468, 2017.
[12] O. Castaneda, S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein, and C. Studer, “1-bit massive mu-mimo precoding in vlsi, ”IEEE Journal on Emerging and Selected Topic in Circuits and Systems, vol. 7, no. 4, 2017.
[13] C. Wang, C. Wen, S. Jin, and S. Tsai, “Finite-alphabet precoding for massive mu-mimo with low-resolution dacs,”IEEE Trans. Wireless Commun., vol. 17, no. 7,pp. 4706–4720, 2018.
[14] M. Shao, Q. Li, and W. Ma, “One-bit massive mimo precoding via a minimun symbol-error probability design, ”in Proc. IEEE Int. Conf.
coust., Speech, Signal Process, pp. 3579–3583, 2018.
[15] C. E. Chen and Y. H. Huang, “Lattice-reduction-aided one-bit precoding for massive mu-mimo systems, ”IEEE Trans. on Vehicular Technology, vol. 68, no. 7, 2019.
[16] F. Sohrabi, Y. Liu, and W. Yu, “One-bit precoding and constellation range design for massive mimo with qam signaling, ”IEEE Journal of Selecting Topics in Signal Processing, vol. 12, no. 3, 2018.
[17] S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein, and C. Studer, “Nonlinear 1-bitprecoding for massive mu-mimo with higher-order modulation,” 2016.
[18] D. Wubben, D. Seethaler, J. Jalden, and G. Matz, “Lattice reduction, ”IEEE Signal Processing Mag., vol. 28, no. 3, pp. 70–91, 2011.
[19] Y. H. Gan, C. Ling, and W. H. Mow, “Complex lattice reduction algorithm for low-complexity full-diversity mimo detection, ”IEEE Tran. Signal Process, vol. 57,no. 7, pp. 2701–2710, 2009.
[20] H. Yao and G. W. Wornell, “Lattice-reduction-aided detectors for mimo communication systems, ”Global Telecommunications Conference, vol. 1, pp. 424–428, 2002.
[21] L. Babai, “On lovasz lattice reduction and the nearest lattice reduction point problem, ”Combinatorica, vol. 6, no. 1, pp. 1–13, 1986.
[22] A. Mezghani, R. Ghiat, and J. A. Nossek, “Transmit processing with low resolution d/a-converters,” pp. 683–686, 2009.
[23] S. Shah, A. K. Yadav, C. D. Castillo, D. W. Jacobs, C. Studer, and T. Goldstein, “Biconvex relaxation for semidefinite programming in computer vision,” pp. 717–735, 2016.
[24] G. H. Golub and C. F. V. Loan, “Matrix computations,” 1996.
[25] M. Wu, B.Yin, G. Wang, C. Dick, J. R. Cavallaro, and C. Studer, “Large-scale mimo detection for 3gpp lte: Algorithm and fpga implementations, ”IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 916–929, 2014.
[26] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers, ”Found. Trends Mach. Learn, vol. 3, no. 1, pp. 1–122, 2011.
[27] C. H. Liao, J. Y. Wang, and Y. H. Huang, “Joint qr decomposition and lattice reduction processor for 8x8 mimo systems, ”in Proc. IEEE ASSCC, 2013.

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