下鏈LTE-A蜂巢式系統中協調式多點傳輸方法之研究

協調式多點傳輸 (Coordinated Multi-Point Transmission，CoMP) 是一種有效降低基地台間干擾的方法。其主要概念是挑選數個基地台彼此合作以消除干擾，這衍生出一個問題：哪些基地台該合作並形成一個協調式多點傳輸叢集 (CoMP Cluster)？針對下鏈傳輸，我們提出一個動態建立叢集的方法。為了降低複雜度，我們使用一種基於區塊對角化 (Block Diagonalization) 的線性前置編碼器。模擬結果顯示出此動態方法優於另一靜態方法。接下來，我們提出一最佳的功率分配方式，使得總傳輸功率最低並同時滿足誤碼率 (Bit Error Rate)、資料傳輸率與天線傳輸功率限制。我們使用Lagrange對偶分解 (Dual Decomposition) 來解決此非凸 (Non-Convex) 的最佳化問題。和一固定的功率分配方式比較後，模擬結果顯示此最佳化方法能提供較佳的效能，此外，各天線上的傳輸功率也較少超出其限制。最後，我們提出了一個降低峰均值功率比(Peak-to-Average Power Ratio，PAPR) 的方法，其使用疊代的方式來改變星座點以降低PAPR。模擬結果顯示僅需兩次的疊代就可獲得很好的PAPR降低效果。

關鍵字

協調式多點傳輸；基地台叢集建立；動態資源分配；峰均值功率比降低

並列摘要

Coordinated multi-point transmission (CoMP) is a promising way to suppress inter-base station (BS) interference. The main idea of CoMP is to select several BSs which could cooperate together to mitigate interference, which raises an intrinsic problem of which BSs should form a CoMP cluster. We propose a dynamic clustering method for downlink transmission, which forms CoMP clusters adaptively. To reduce the complexity, a linear precoder based on block diagonalization (BD) is used throughout this thesis. Simulation results show that our dynamic scheme outperforms another static method. Next, we design an optimal power allocation method that minimizes the total transmit power while satisfying bit error rate (BER), user rate requirement and per-antenna power constraints. Lagrange dual decomposition is used to solve this non-convex optimization problem. The numerical results reveal the great performance gain against fixed power allocation, and the transmit power on each antenna seldom exceeds the power limit. Finally, we propose a peak-to-average power ratio (PAPR) reduction method, which reduces signal peak by altering signal constellations. The simulation results show large PAPR reduction using an iterative procedure with only two iterations.

並列關鍵字

CoMP ； base station clustering ； adaptive resource allocation ； PAPR reduction

參考文獻

[1] S. Catreux, P. F. Driessen, and L. J. Greenstein, “Simulation results for an interferencelimited multiple-input multiple-output cellular system,” IEEE Commun. Lett., vol. 4, no. 11, pp. 334–336, Nov. 2000.

[2] D. Gesbert, S. Hanly, H. Huang, S. Shamai, O. Simeone, and W. Yu, “Multicell MIMO cooperative networks: A new look at interference,” IEEE J. Select. Areas Commun., vol. 28, no. 9, pp. 1380–1408, Dec. 2010.

[3] M. K. Karakayali, G. J. Foschini, and R. A. Valenzuela, “Network coordination for spectrally efficient communications in cellular systems,” IEEE Wireless Commun., vol. 13, no. 4, pp. 56–61, Aug. 2006.

[4] H. Zhang and H. Dai, “Cochannel interference mitigation and cooperative processing in downlink multicell multiuser MIMO networks,” EURASIP J. Wireless Commun., pp. 222–235, Dec. 2004.

[6] P. Marsch and G. Fettweis, “A framework for optimizing the downlink performance of distributed antenna systems under a constrained backhaul,” in Proc. European Wireless Conf. (EW), Paris, France, Apr. 2007.

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下鏈LTE-A蜂巢式系統中協調式多點傳輸方法之研究

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