臺灣博碩士論文加值系統

在現今無線傳輸上，為了提升資料吞吐量，大規模多輸入多輸出 (Massive Multiple Input Multiple Output, Massive MIMO)及多使用者(Multiusers)已被視為下一世代5G的標準，其目的以提高資料傳輸量和系統可靠度。並且結合採用單載波分頻多重存取 (Single Carrier Frequency Division Multiple Access, SC-FDMA) 為其上行鏈路傳輸技術，以達到對於資料傳輸速率與系統效能的需求。因單載波分頻多重存取技術結合了正交分頻多重存取 (Orthogonal Frequency Division Multiple Access, OFDMA) 的良好特性，並且相較之下具有較低的峰值功率比 (Peak-to-Average Power Ratio, PAPR)。
本篇論文主要是研究多使用者(Multiuser)，以及超大型多輸入多輸出(Massive MIMO)，在接收機架構上，利用卡曼通道估測(Kalman Channel estimation)取代已知通道，等化器的部分採用MMSE(Minimum Mean Square Error)，為了解決超大型多輸入多輸出的通道資訊龐大的複雜度問題，因此使用紐曼集數展開(Neumann Series Expansion)、SOR(Successive Overrelaxation)、SSOR(Symmetric Successive Overrelaxtion)能有效降低複雜度。並且結合渦輪碼 (Turbo Codes) 的多輸入多輸出單載波分頻多重存取系統，利用最大事後機率 (Maximum A Posteriori, MAP) 所提供之外部資訊 (Extrinsic Information) 作為多使用者干擾消除。再配合迭代的方式，重複執行卡曼通道估測、等化器、多使用者干擾消除、以及渦輪等化器，以得到更好的位元錯誤率。最後的模擬顯示了迭代的效果，還有比較不同等化器間的收斂速度、計算複雜度以及位元錯誤率的差異。

With the increasing requirement of data throughput services in wireless transmission nowadays, Massive Multiple-Input Multiple-Output (Massive MIMO) and Multiusers technologies are adopted in many standards to enhance the data rate and the link robustness. And the combination of the SC-FDMA (Single Carrier Frequency Division Multiple Access) modulation scheme for the uplink transmission scheme, to approach the demand for data transmission rates and error performance. Because of, SC-FDMA combines the desirable characteristics of OFDMA with the low PAPR (Peak-to-Average Power Ratio) of single-carrier transmission schemes. In this paper, we use low complexity turbo detectors to solve the problem of huge channel matrix at receiver side, we use the detectors by Neumann Series Expansion, SOR (Successive Overrelaxation), SSOR (Symmetric Successive Overrelaxtion), and combines the Turbo codes, Kalman channel estimation for Massive MIMO SC-FDMA systems. Then utilize the Extrinsic Information from Maximum A Posteriori (MAP) decoder to cancel the Multiple Access Interference (MAI). Simulations for an uplink scenario assess the proposed detectors methods in several situations, iteration result, computational complexity and convergence speed.

Chapter 1 Introduction 1
Chapter 2 Background 5
2.1 Single Carrier FDMA 5
2.2Massive MIMO techniques 7
2.3 Low Complexity Signal Detectors 8
2.4 Spatial Channel Model (SCM) 8
Chapter 3 Transmitter and Receiver of Massive MIMO SC-FDMA Systems 12
3.1 System Description 12
Chapter 4 Kalman Channel Estimation with Turbo Detectors 16
4.1 Kalman Channel Estimation 16
4.2Multiuser Detection 21
4.3 Turbo Equalization 22
Chapter 5 Low complexity Signal Detectors Based on MMSE in Massive MIMO system 24
5.1 Nuemann Series Expansion 24
5.2 SOR Signal Detector 26
5.3 SOR Computaional Complexity Analysis 27
5.4 SSOR Signal Detector 28
5.5 SSOR Computaional Complexity Analysis 29
5.6 Quantified Relaxation Parameter 30
Chapter 6 Simulation Result 31
Chapter 7 Conclusion 48
Bibliography 50

[1]Larsson, E., Edfors, O., Tufvesson, F., Marzetta, T., “Massive MIMO for next generation wireless systems,” IEEE Communications Magazine, pp. 186-195, Feb. 2014
[2]Michael Wu, Bei Yin, Guohui Wang, Chris Dick, Joseph R. Cavallaro, and Christoph Studer,“Large-Scale MIMO Detection for 3GPP LTE:Algorithms and FPGA Implementations,” IEEE Journal Of Selected Topics In Signal Processing, Vol. 8, No. 5,pp.916-929, OCTOBER 2014
[3]Koetter, R.; Singer, A.C.; Tüchler, M., “Turbo equalization”, Signal Processing Magazine, VOL. 21, IEEE, Jan. 2004
[4]Single Carrier FDMA: A New Air Interface for Long Term Evolution ,Hyung G. Myung and David J. Goodman © 2008 John Wiley & Sons, Ltd. ISBN: 978-0-470-72449-1
[5]A.Paulraj, R. Nabar, and D. Gore, Introduction to Space-Time Wireless Communications. New York, USA: Cambridge University Press, 2008.
[6]3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio ACCESS (E-UTRA); Multiplexing and channel coding (Release 9). 3GPP Organizational Partners TS 36.212 Rev. 8.3.0, May 2008.
[7]S. Sesia, I. Toufik, and M. Baker, LTE, the UMTS Long Term Evolution: Form Theory to Practice. Wiley Publishing, 2009.
[8]3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio ACCESS (E-UTRA); Physical Layer Procedures (Release 10). 3GPP Organizational Partners TS 36.213 version 10.10.0, Jul. 2013.
[9]IEEE Draft Standard Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications: Amendment 4: Enhancements for Higher Throughput. P802.11n_D3.00, Sept. 2007.
[10]T.L. Marzetta, “Non-cooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590-3600, Nov. 2010.
[11]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 large arrays,” IEEE Signal Process. Mag., vol. 30, no. 1, pp. 40-60, Jan. 2013.
[12]Y.-H. Nam, B. L. Ng, K. Sayana, Y. Li, J. Zhang, Y. Kim, and J. Lee, “Full-dimension MIMO (FD-MIMO) for next generation cellular technolohgy,” IEEE Commun. Mag., vol. 51, no. 6, pp. 172-179, Jun. 2013.
[13]Xinyu Gao; Linglong Dai; Yuting Hu; Yu Zhang; Zhaocheng Wang, “Low-Complexity Signal Detection for Large-Scale MIMO in optimal Wireless Communications,” IEEE Journal on Selected Areas in Communications. Volume. 33, Issue: 9, 2015.
[14]Xinyu Gao; Linglong Dai; Yuting Hu; Zhongxu Wang; Zhaocheng Wang, “Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems,” 2014 IEEE Global Communications Conference, Pages: 3291-3295, 2014.
[15]Jiaqi Ning; Zhaohua Lu; Tian Xie; Jinguo Quan, “Low complexity signal detector based on SSOR method for massive MIMO systems,” 2015 IEEEE International Symposium on Broadband Multimedia Systems and Broadcasting, Pages:1-4, 2015.
[16]Muralidhar, K.; Sreedhar, D. "Generalized vector state-scalar observation Kalman channel estimator for doubly-selective OFDM systems", Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, On page(s): 4928 - 4932
[17]Xin Li and Wong T.F. “Turbo equalization with nonlinear Kalman filtering for time-varying frequency-selective fading channels” Wireless Communications, IEEE Transactions pages: 691 - 700, on year 2007
[18]Xiaofei Zhang; Hua Rui; Dazhuan Xu, “A low-complexity multiuser detection method based on mean field annealing,” Communications, Circuits and Systems, 2004. ICCCAS 2004. 2004 International Conference on. Page: 79-83.Vol. 1.2004.
[19]Kyeongyeon Kim, Kalantarova, N., Kozat, S.S., Singer, A.C., “Linear MMSE-Optimal Turbo Equalization Using Context Trees”, IEEE Transactions On Signal Processing, Vol. 61, No. 12, pp. 3041-3055, Jun. 15, 2013
[20]Spatial Channel Model for Multiple Input Multiple Output (MIMO) Simulations A Ray Tracing Simulator Based on 3GPP TR 25.996 v. 6.1.0, Developed by: Ioannis A. Xirouchakis, Undergraduate Student, Physics Department, University of Athen
[21]Byunggi Kang; Ji-Hwan Yoon; Jongsun Park, “Low Complexity massive MIMO detection architecture based on Neumann method,” 2015 International SoC Design Conference (IOSCC).Pages: 293-297.2015.
[22]Michael Tüchler, Andrew C. Singer, Ralf Koetter, "Minimum Mean Squared Error Equalization Using A Priori Information," IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 50, NO. 3, MARCH 2002
[23]Amirhossein Rafati, Huang Lou, and Chengshan Xiao, "Soft-Decision Feedback Turbo Equalization for LDPC-Coded MIMO Underwater Acoustic Communications," IEEE journal of oceanic engineering,vol.39,no.1,Jan. 2014
[24]S. Lee, N. Shanbhag, and A. Singer, "Area-efficient high-throughput VLSI architecture for MAP-based turbo equalizer," in Proc. IEEE Signal Process. Syst.: Design Implementation pp. 87-92, Aug. 2003.
[25]Kun Young Chung, Gupta, S.K. , "Structural Delay Testing Under Restricted Scan of Latch-based Pipelines with Time Borrowing," Test Conference, 2003. Proceedings. ITC 2003. International
[26]Rana, M.M. ; Kim, J., "LMS based blind channel estimation of SC-FDMA systems using variable step size and phase information," ELECTRONICS LETTERS 3rd March 2011 Vol. 47 No. 5
[27]Stephan ten Brink, "Convergence Behavior of Iteratively Decoded Parallel Concatenated Codes," IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 49, NO. 10, OCTOBER 2001
[28]Lyu, Yibo ; Cai, Guofa ; Wang, Lin, "Iterative Demodulation and Decoding of LDPC-coded M-ary DCSK Modulation over AWGN Channel" Medical Information and Communication Technology (ISMICT), 2014 8th International Symposium
[29]Cheema, Sher Ali ; Grossmann, Marcus ; Landmann, Markus ;Galdo, Giovanni Del ; Galdo, Giovanni Del , "Link Adaptation for LTE-A Systems Employing MMSE Turbo Equalization" Smart Antennas (WSA), 2014 18th International ITG Workshop.
[30]Ahmed Hesham Mehana; Aria Nosratinia “Diversity of MMSE MIMO Receivers,” IEEE Transactions on Information Theory. Vol: 58 ,Issue: 11, Pages : 6788-6805. 2012.