多使用者多輸入多輸出正交分頻多工系統使用基因演算法之功率配置

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姓名

施震宇(Chen-Yu Shih) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

多使用者多輸入多輸出正交分頻多工系統使用基因演算法之功率配置
(Genetic Algorithm-based Power Control for MIMO-OFDM Femtocell Networks)

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摘要(中)

本文提出波束成型與功率配置演算法在，目標為考慮巨微細胞使用者在鄰近毫微微細胞的情況下使得吞吐量總和最大化，應用在多輸入多輸出正交分頻多工下鏈異質網路系統。因為巨微細胞和毫微微細胞使用相同的頻帶，所以衍生出了同頻帶干擾的問題。在多輸出正交分頻多工系統下，因為找尋最佳使用者之集合計算複雜度過於龐大，本文假設毫微微細胞使用者的數目是小於天線個數的。然而干擾存在下之功率配置方式不再與傳統注水式功率配置演算法相同。
在這個議題下，問題將不再是凸函數之問題，我們無法用一般線性的方法去解決，我們提出了使用基因演算法處理干擾存在下功率配置的問題。除此之外，我也利用波束形成的方法去降低同頻帶之間的干擾。由電腦模擬分析，我們可以知道提出次佳演算法的效能是優於平均分配功率的方法。

摘要(英)

In this thesis, the beamforming and power allocation algorithm is proposed for downlink MIMO-OFDM system, where the objective considers is the maximization of the data rate of Marco user that near to the Femto base station. Since the Marco and Femto base station share the same bandwidth, there is a co-channel interference problem. In the MIMO system, finding the optimal user subset has very high complexity, we assume that Femto users are less than the number of antennas. However, the power allocation is not in interference-free environment, we can’t use the traditional water filling method in the SDMA system.
In this scenario, the problem in two-tier OFDM Femto networks is not a convex problem, and we can’t use the linear method to solve it. We propose a power allocation method based on the Genetic algorithm to overcome this problem with co-channel interference. In addition, we also use the Zero-Forcing beamforming design to alleviate the co-channel interference. The computer simulation results show that the proposed sub-optimal scheme is better than the normal power allocation scheme.

關鍵字(中)

★ 正交多頻
★ 基因
★ 多天線
★ 功率配置

關鍵字(英)

★ OFDM
★ genetic
★ power control
★ MIMO

論文目次

List of Figures - 1 -
1 Introduction - 1 -
1.1 A history of Wireless Communication - 1 -
1.2 Heterogeneous Networks - 1 -
1.3 Multicarrier Modulation (OFDM) - 3 -
1.4 Multiple Input Multiple Output (MIMO) - 7 -
1.4.1 Introduction to MIMO - 7 -
1.4.2 MMSE and Zero-Forcing beamforming - 8 -
1.5 Organization - 9 -
1.6 Notation - 9 -
1.7 Abbreviations - 10 -
2 System Model and Problem Formulation - 11 -
2.1 System Model - 11 -
2.2 Beamforming Design - 14 -
2.3 Problem Formulation - 17 -
3 Power Allocation Scheme with Genetic Algorithm - 19 -
3.1 Water-filling Power Allocation - 19 -
3.2 Proposed Algorithm - 21 -
4 Simulation results - 29 -
5 Conclusion - 32 -
6 BIBLIOGRAPHY - 33 -

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指導教授

陳永芳(Yung-Fang Chen)

審核日期

2012-7-19

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