多輸入多輸出波束合成中基於隱藏式馬可夫模型之多用戶選擇機制

在多用戶多重輸入多重輸出的研究中核心技術有用戶選擇機制以及波束合成技術，主要是利用用戶所回饋的通道訊息來判斷用戶間關係，找出一組最佳的用戶集，接著利用這些通道訊息計算出適合用戶的波束。然而，像是衰落資訊在實際上是不太可能回饋給基站，或者說就算回饋給基站，其值也會經過量化，因此用戶選擇機制的效能便會降低，甚至波束合成也會受影響，因此本論文提出不需依據衰落資訊並搭配定向波束來完成用戶與波束共同排程的機制。而在時變的情況下，為了使波束切換速度的缺點不影響系統效能，本論導入隱藏式馬可夫模型，因為隱藏式馬可夫模型是一種機率統計模型，可藉由推估用戶通道的轉移狀態並預測其轉移程度來獲知用戶間的未來通道變化程度，多用戶選擇機制藉由選擇時間關聯性高的用戶，使得系統容量在連續採用同組用戶集及波束權重做通訊時不會有太大變化，因此就不需每個時槽下啟用用戶選擇機制，回饋資訊也因此降低。

關鍵字

多用戶多重輸入多重輸出；隱藏式馬可夫模型

並列摘要

The core technology research in Multi User - Multiple Input Multiple Output (MU-MIMO) is user selection mechanism and beam forming technology. It mainly utilizes the messages through feedback channel to know the spatial relationship among users and to find out best user set. Then, base station will calculate the suitable beam pattern for these users by those channel messages. However, some channel messages like channel fading are impossible to feedback. Even if feedback those messages are possible, the quantization technology would be adopted. Then, the performance of user selection mechanism would decrease even beam forming technology. Thus, this thesis proposes one technology which does not depend on these unreliable messages, and it adopts fixed beam to co-schedule users and beam patterns. In time-vary situation, in order to solve the performance of beam switching speed which does not affect system performance. This thesis introduces the Hidden Markov Model (HHM) which is a probability model. By estimating transition state of user’s channel and predicting transition degree, it can obtain the degree of channel’s change. Then, the user selection mechanism can select the users which have higher time correlation. Hence, the system capacity would not change rapidly, when the same user set and beam weights are communicating for some time slots. Thus, the user selection mechanism and beam forming technology are not necessary to change every slot, even reduce the feedback information.

並列關鍵字

Multi User - Multiple Input Multiple ； Hidden Markov Model

參考文獻

[2]S. S. Jeng, G. T. Okamoto, G. Xu, H. P. Lin, and W. J. Vogel, “Experimental Evaluation of Smart antenna System Performance for Wireless Communications,” IEEE Trans. on Antennas and Propagation, vol. 46, no. 6, pp. 749-757, July 1998.

[3]G. Caire and S. Shamai, “On the achievable throughput of a multiantenna Gaussian broadcast channel,” IEEE Trans. On Information theory, vol. 49, pp. 1691-1706, July 2003.

[4]L. P. Ping, P. M. On, Y. W. P. Hong, and C. C. J. Kuo, “Optimized Opportunistic Multicast Scheduling (OMS) over Wireless Cellular Networks,” IEEE Trans. on Wireless communications, vol. 9, no. 2, pp. 791-801, Feb. 2010.

[5]S. Shreeram, W. A. Krzymie´n, “Simplified Fair Scheduling and Antenna Selection Algorithms for Multiuser MIMO Orthogonal Space-Division Multiplexing Downlink,” IEEE Trans. on vehicular technology, vol. 58, no. 3, pp. 1329-1344, Mar. 2009.

[6]P. Viswanath, and N. C. David, and L. Rajiv “Opportunistic Beamforming Using Dumb Antennas,” IEEE Trans. On Information theory, vol. 48, no. 6, pp.1277-1294, June 2002.

國際替代計量

多輸入多輸出波束合成中基於隱藏式馬可夫模型之多用戶選擇機制

未授權

主題瀏覽