臺灣博碩士論文加值系統

大規模多輸入多輸出系統在5G無線通訊中，因為具有突出的頻寬效率及能量效率，被視為非常有潛力的技術。
然而，在分頻多工下要獲取通道資訊需要極為大量的下行訓練及上行回傳，因此分頻多工大規模多輸入多輸出系統一直被視為不實際的方法。
本篇論文提出了一個適用於分頻多工大規模多輸入多輸出系統且不需使用通道回傳的多使用者傳輸機制，可以達到比以往低上許多的延遲時間。
該機制的下行預編碼設計使用了分頻多工的通道互易性及從上行獲得的部分通道資訊。
此外，該機制利用空時分組碼以避免通道回授，並採用了空間上的濾波設計達到抑制干擾，同時加強對部分通道資訊預估誤差的穩定性。
本文呈現了兩種功率分配的方案，分別為了最小化所有使用者之最大錯誤率及最大化使用者傳輸效率和。
模擬結果顯示了所提出的方法在使用者傳輸效率和及錯誤率效能方面的優勢。

Massive MIMO is a promising technique for the next-generation wireless communication systems due to its tremendous performances in spectral and energy efficiency.
Channel state information (CSI) acquisition for massive MIMO operated under frequency-division duplex (FDD) is widely regarded as a challenging task due to enormous overhead of downlink training and uplink feedback.
In this paper, a multiuser downlink precoding mechanism for FDD massive MIMO that does not require any explicit feedback of downlink CSI is proposed, which contributes to a much lower latency compared to the previous mechanisms.
The proposed mechanism exploits the reciprocity of FDD systems, and uses only partial CSI obtained from uplink transmissions as the basis of downlink precoding design.
Besides, the proposed mechanism employs space-time block code (STBC) techniques to avoid CSI feedback, and adopts a spatial filter design not only to suppress co-channel interference, but also to increase robustness against estimation error of partial CSI.
Two power allocation schemes aiming at minimizing maximum symbol error rate (SER) of all UEs and maximizing sum rate are also presented.
Simulation results demonstrate that the proposed mechanism achieves satisfactory SER and sum rate performances.

誌謝 ii
摘要 iii
Abstract iv
List of Figures vi
List of Tables vii
Abbreviations and Symbols ix
1 Introduction 1
1.1 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Organization and Notations . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 System Model 6
2.1 Downlink Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Uplink Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 FDD Reciprocity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Proposed Downlink Transmission Scheme Based on P-STBC . . . . . . . 12
3 Problem Statement 16
3.1 Receiver SNR Using Interference-Eliminating Precoders . . . . . . . . . 18
3.2 Problem Formulation I: Maximum SER Minimization . . . . . . . . . . . 20
3.3 Problem Formulation II: Sum Rate Maximization . . . . . . . . . . . . . 21
4 Proposed Method 22
4.1 Beamformer Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2 Power Allocation for Maximum SER Minimization . . . . . . . . . . . . 25
4.3 Special Case with Closed-Form Solution: Rician Conditional pdf with
Fixed UE Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.4 Power Allocation for Sum Rate Maximization . . . . . . . . . . . . . . . 29
4.5 Summary of the Proposed Method . . . . . . . . . . . . . . . . . . . . . 30
4.6 Computational Complexity Analysis . . . . . . . . . . . . . . . . . . . . 31
5 Simulation Results 32
5.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2 Simulation of maximum SER minimization . . . . . . . . . . . . . . . . 35
5.3 Simulation of Sum Rate Maximization . . . . . . . . . . . . . . . . . . . 41
6 Conclusion 44
A Proof of Proposition 1 46
Bibliography 48

[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 Large Arrays,” vol. 30, no. 1, pp. 40–60, Jan. 2013.
[2] F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski, “Five Disruptive Technology Directions for 5G,” vol. 52, no. 2, pp. 74–80, Feb. 2014.
[3] E. G. Larsson, O. Edfors, F. Tufvesson, and T. L. Marzetta, “Massive MIMO for next generation wireless systems,” vol. 52, no. 2, pp. 186–195, Feb. 2014.
[4] T. L. Marzetta, “Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas,” vol. 9, no. 11, pp. 3590–3600, Nov. 2010.
[5] E. Bjornson and E. G. Larsson, “Massive MIMO: ten myths and one critical question,” vol. 54, no. 2, pp. 114–123, Feb. 2016.
[6] P. W. C. Chan et al., “The evolution path of 4G networks: FDD or TDD?” vol. 44, no. 12, pp. 42–50, Dec. 2006.
[7] A. Adhikary, J. Nam, J. Y. Ahn, and G. Caire, “Joint Spatial Division and Multiplexing: The Large-Scale Array Regime,” vol. 59, no. 10, pp. 6441–6463, Oct. 2013.
[8] J. Choi, D. J. Love, and P. Bidigare, “Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory,” vol. 8, no. 5, pp. 802–814, Oct. 2014.
[9] J. Fang et al., “Low-Rank Covariance-Assisted Downlink Training and Channel Estimation for FDD Massive MIMO Systems,” vol. 16, no. 3, pp. 1935–1947, Mar. 2017.
[10] X. Rao and V. K. N. Lau, “Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems,” vol. 62, no. 12, pp. 3261–3271, Jun. 2014.
[11] J.-C. Shen, J. Zhang, E. Alsusa, and K. B. Lataief, “Compressed CSI Acquisition in FDD Massive MIMO: How Much Training is Needed?” vol. 15, no. 6, pp. 4145–4156, Jun. 2016.
[12] Y. Han, J. Lee, and D. J. Love, “Compressed Sensing-Aided Downlink Channel Training for FDD Massive MIMO Systems,” vol. PP, no. 99, pp. 1–1, 2017.
[13] X. Xiong, X. Wang, X. Gao, and X. You, “Beam-domain Channel Estimation for FDD Massive MIMO Systems with Optimal Thresholds,” vol. PP, no. 99, pp. 1–1, 2017.
[14] U. Ugurlu, R. Wichman, C. B. Ribeiro, and C. Wijting, “A Multipath Extraction-Based CSI Acquisition Method for FDD Cellular Networks With Massive Antenna Arrays,” vol. 15, no. 4, pp. 2940–2953, Apr. 2016.
[15] W. Shen, L. Dai, B. Shim, Z. Wang, and R. W. Heath, “Channel feedback based on aod-adaptive subspace codebook in fdd massive mimo systems,” cs.IT, vol. abs/1704.00658, 2017. [Online]. Available: https://arxiv.org/abs/1704.00658
[16] X. Luo et al., “A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning,” vol. PP, no. 99, pp. 1–1, 2017.
[17] K. Hugl, K. Kalliola, and J. Laurila, “Spatial reciprocity of uplink and downlink radio channels in FDD systems,” Proc. COST 273 Technical Document TD (02), vol. 66, p. 7, 2002.
[18] R. Schmidt, “Multiple emitter location and signal parameter estimation,” vol. 34, no. 3, Mar. 1986.
[19] M. Shaghaghi and S. A. Vorobyov, “Subspace Leakage Analysis and Improved DOA Estimation With Small Sample Size,” vol. 63, no. 12, pp. 3251–3265, Jun. 2015.
[20] R. Cao, B. Liu, F. Gao, and X. Zhang, “A Low-Complex One-Snapshot DOA Estimation Algorithm with Massive ULA,” vol. 21, no. 5, pp. 1071–1074, May 2017.
[21] R. B. Ertel et al., “Overview of spatial channel models for antenna array communication systems,” IEEE Personal Communications, vol. 5, no. 1, pp. 10–22, Feb 1998.
[22] Q. Spencer, B. Jeffs, and M. Jensen, “Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel,” vol. 18, no. 3, pp. 347–360, Mar. 2000.
[23] O. E. Ayach, S. Rajagopal, S. Abu-Surra, and Z. P. nad Robert W. Heath, “Spatially Sparse Precoding in Millimeter Wave MIMO Systems,” vol. 13, no. 3, pp. 1499–1513, Mar. 2014.
[24] N. Czink, X. Yin, H. Herdin, E. Bonek, and B. H. Fleury, “Cluster characteristics in a MIMO indoor propagation environment,” vol. 6, no. 4, pp. 1536–1276, Apr. 2007.
[25] S. Sun, T. S. Rappaport, R. W. Heath, A. Nix, and S. Rangan, “Mimo for millimeterwave wireless communications: beamforming, spatial multiplexing, or both?” vol. 52, no. 12, pp. 110–121, Dec. 2014.
[26] A. Forenza, D. J. Love, and R. W. Heath, “Simplified Spatial Correlation Models for Clustered MIMO Channels With Different Array Configurations,” vol. 56, no. 4, pp. 1924–1934, Jul. 2007.
[27] D. Tse and P. Viswanath, Fundamentals of Wiresless Communication. Cambridge University Press, 2005.
[28] A. Alkhateeb, O. E. A. abd Geert Leus, and R. W. Heath, “Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems,” vol. 8, no. 5, pp. 831–846, Oct. 2014.
[29] 3GPP, “Study on elevation beamforming/full-dimension (FD) MIMO for LTE (Release 13),” TR36.897, V13.0.0, June 2015.
[30] M. C. Vanderveen, A. J. V. der Veen, and A. Paulraj, “Estimation of multipath parameters in wireless communications,” vol. 46, no. 3, pp. 682–690, Mar. 1998.
[31] Y.-Y. Wang, J.-T. Chen, and W.-H. Fang, “TST-MUSIC for joint DOA-delay estimation,” vol. 49, no. 4, pp. 721–729, Apr. 2001.
[32] M.-F. Tang, C.-C. Chen, and B. Su, “Downlink path-based precoding in FDD massive MIMO systems without CSI feedback,” in Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016 IEEE, Jul. 2016.
[33] V. Tarokh, H. Jafarkhani, and A. R. Calderbank, “Space-time block codes from orthogonal designs,” vol. 45, no. 5, pp. 1456–1467, Jul. 1999.
[34] G. Klang and B. Ottersten, “Interference robustness aspects of space-time block code-based transmit diversity,” vol. 53, no. 4, pp. 1299–1309, Apr. 2005.
[35] X. Li, T. Luo, G. Yue, and C. Yin, “A squaring method to simplify the decoding of orthogonal space-time block codes,” vol. 49, no. 10, pp. 1700–1703, Oct. 2001.
[36] A. Goldsmith, Wireless Communications. Cambridge University Press, 2005.
[37] M. K. Simon and M.-S. Alouini, “A Unified Approach to the Performance Analysis of Digital Communication over Generalized Fading Channels,” Proceedings of the IEEE, vol. 86, no. 9, pp. 1860–1877, Sep. 1998.
[38] S. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.
[39] M.-F. Tang and B. Su, “Downlink precoding for multiple users in FDD massive MIMO without CSI feedback,” Journal of Signal Processing Systems, no. 2, pp. 151–163, 2015, [Online]. Available: http:/dx.doi.org/10.1007/s11265-015-1079-0.
[40] L. J. Karam and J. H. McClellan, “Complex Chebyshev approximation for FIR filter design,” vol. 42, no. 3, pp. 207–216, Mar. 1995.
[41] M. Grant and S. Boyd, “CVX: Matlab Software for Disciplined Convex Programming, version 2.1,” http://cvxr.com/cvx, Mar. 2014.
[42] C.-W. Hsu, M.-F. Tang, and B. Su, “Power allocation for downlink path-based precoding in multiuser FDD massive MIMO systems without CSI feedback,” 2016 50th Asilomar Conference on Signals, Systems and Computers, Nov. 2016.
[43] M. Colombo and J. Gondzio, “Further development of multiple centrality correctors for interior point methods,” Computational Optimization and Applications, vol. 41, no. 3, pp. 277–305, 2008.
[44] Q. Spencer, A. Swindlehurst, and M. Haardt, “Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels,” vol. 52, no. 2, pp. 461–471, Feb. 2004.
[45] J. Brady, N. Behdad, and A. M. Sayeed, “Beamspace MIMO for Millimeter-Wave Communications: System Architecture, Modeling, Analysis, and Measurements,” vol. 61, no. 7, pp. 3814–3827, Jul. 2013.
[46] Z. Gao, L. Dai, W. Dai, B. Shim, and Z. Wang, “Structured Compressive Sensing-Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO,” vol. 64, no. 2, pp. 601–617, Feb. 2016.