由於能夠有效的提高頻譜效益，因此大規模多輸入多輸出系統成為了第五代行動通訊系統主要的無線通信技術之一，但要達到大規模多輸入多輸出的頻譜效應，基地台必須要獲得與用戶之間的的通道狀態資訊。在頻分雙工系統中，用戶必須估計通道狀態並且回報給基地台，由於通道狀態資訊隨著天線數量的增加而成長，因此在大規模多輸入多輸出系統中，回報通道狀態資訊佔用大量頻寬導致資訊吞吐量下降。在本篇論文中，我們提出了一種基於壓縮感知的方法，以降低兩階段干擾消除中，用戶回報通道共變異數矩陣的開銷。利用這個壓縮技術在用戶端壓縮通道共變異數矩陣並回傳給基地台，基地台在可以接受的準確度中重構出通道共變異數矩陣。此外，我們將系統擴展到多個基地台，並且有效的消除基地間干擾以維持服務品質，最後利用模擬結果評估所提出方法的性能並與現有方法進行比較。

Massive multiple-input multiple-output (MIMO) is an emerging approach for wireless communications thanks to its energy efficiency and high system capacity. The benefits of massive MIMO systems can be realized only when channel state information (CSI) is available at the transmitter. In frequency division duplexing (FDD) operation, users have to feed back CSI to the transmitter. Since the amount of CSI grows with the number of antennas, CSI feedback overhead becomes extraordinarily large in massive MIMO systems. In this work, we proposed a new approach based on compressive sensing to reduce the long-term CSI overhead for the two-stage feedback system. The proposed approach permits the transmitter to obtain long-term CSI with acceptable accuracy while substantially reducing feedback overhead. Besides, we extend the scenario to multi-cell system where inter-cell interference(ICI) must be canceled to maintain desired service quality. Two approaches are considered to eliminate ICI based on different CSI feedback mechanisms. Simulation results are presented to evaluate the performance of the proposed methods in comparison with some existing approaches.

[1] L. Liang, W. Xu, and X. D. Dong, “Low-complexity hybrid precoding in massive multiuser mimo systems,” IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653–656, 2014.
[2] A. F. Molisch, V. V. Ratnam, S. Q. Han, Z. D. Li, S. L. H. Nguyen, L. S. Li, and K. Haneda, “Hybrid beamforming for massive mimo: A survey,” IEEE Communications Magazine, vol. 55, no. 9, pp. 134–141, 2017.
[3] W. H. Ni and X. D. Dong, “Hybrid block diagonalization for massive multiuser mimo systems,” IEEE Transactions on Communications, vol. 64, no. 1, pp. 201– 211, 2016.
[4] X. Y. Wu, D. P. Liu, and F. F. Yin, “Hybrid beamforming for multi-user massive mimo systems,” IEEE Transactions on Communications, vol. 66, no. 9, pp. 3879– 3891, 2018.
[5] A. Adhikary, J. Nam, J. Y. Ahn, and G. Caire, “Joint spatial division and multiplexing-the large-scale array regime,” IEEE Transactions on Information Theory, vol. 59, no. 10, pp. 6441–6463, 2013.
[6] Y. Jeon, C. Song, S. R. Lee, S. Maeng, J. Jung, and I. Lee, “New beamforming designs for joint spatial division and multiplexing in large-scale miso multi-user systems,” IEEE Transactions on Wireless Communications, vol. 16, no. 5, pp. 3029–3041, 2017.
[7] J. Nam, A. Adhikary, J. Y. Ahn, and G. Caire, “Joint spatial division and multiplexing: Opportunistic beamforming, user grouping and simplified downlink scheduling,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 876–890, 2014.
[8] R. G. Baraniuk, “Compressive sensing,” IEEE Signal Processing Magazine, vol. 24, no. 4, pp. 118–+, 2007. [9] E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions
on Information Theory, vol. 52, no. 2, pp. 489–509, 2006.
[10] P. H. Kuo, H. Kung, and P. A. Ting, “Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays,” Proc. IEEE WCNC, pp. 492–497, 2012.
[11] W. Q. Shen, L. L. Dai, Y. Shi, X. D. Zhu, and Z. C. Wang, “Compressive sensing-based differential channel feedback for massive mimo,” Electronics Letters, vol. 51, no. 22, pp. 1824–1825, 2015.
[12] M. S. Sim, J. Park, C. B. Chae, and R. W. Heath, “Compressed channel feedback for correlated massive mimo systems,” Journal of Communications and Networks, vol. 18, no. 1, pp. 95–104, 2016.
[13] 3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz,” 3rd Generation Partnership Project (3GPP), Technical Report (TR) 38.901, 01 2018, version 14.3.0.
[14] S. Venkatesan, A. Lozano, and R. Valenzuela, “Network mimo: Overcoming intercell interference in indoor wireless systems,” pp. 83–87, Nov 2007.
[15] W. Hardjawana, B. Vucetic, and Y. Li, “Mimo inter-cell interference management through base station cooperation,” pp. 1–5, June 2011.
[16] A. S. Hamza, S. S. Khalifa, H. S. Hamza, and K. Elsayed, “A survey on intercell interference coordination techniques in ofdma-based cellular networks,”IEEE Communications Surveys and Tutorials, vol. 15, no. 4, pp. 1642–1670, 2013.
[17] Z. J. Bai, B. Badic, S. Iwelski, T. Scholand, R. Balraj, G. Bruck, and P. Jung,“On the equivalence of mmse and irc receiver in mu-mimo systems,”IEEE Communications Letters, vol. 15, no. 12, pp. 1288–1290, 2011.
[18] R. Fedrizzi, L. Goratti, K. Gomez, and T. Rasheed, “A novel geometric handover model for aerial 4g networks with wifi-based x2 interface,” Transactions on Emerging Telecommunications Technologies, vol. 28, no. 3, 2017.
[19] Z. G. Pan, K. K. Wong, and T. S. Ng, “Generalized multiuser orthogonal space-division multiplexing,” IEEE Transactions on Wireless Communications, vol. 3, no. 6, pp. 1969–1973, 2004.