臺灣博碩士論文加值系統

本篇論文提出了基因群聚最佳化(Genetical Swarm Optimization, GSO)演算法應用在多重輸入多重輸出系統之訊號偵測。多重輸入多重輸出的技術在無線通訊上是非常的受到注意的，因為MIMO系統提供了明顯的資料傳輸率以及連接的範圍，而不需要增加頻寬以及傳送端的功率。
近年來，在文獻中提出了最佳化(Optimum)的多重輸入多重輸出訊號偵測(MIMO Signal Detection)演算法以及許多的次佳(Suboptimal)多重輸入多重輸出訊號偵測演算法。最佳化的訊號偵測部分，提出了最大相似偵測法(Maximum Likelihood Detection, MLD)，而次最佳化部分也包含了基因演算法(Genetic Algorithm, GA)及粒子群聚最佳化(Particle Swarm Optimization, PSO)演算法，而我們所提出的GSO偵測方法則是利用了基因的進化以及族群之間的共同智慧，來達到比ML稍微降低一點的位元錯誤率(Bit Error Rate, BER)，但是卻可以得到比ML大大降低的運算複雜度。

A genetical swarm optimization (GSO)-based detector using multi-input multi-output (MIMO) spatial multiplexing signaling is proposed. MIMO technology has attracted attention in wireless communications, since it offers significant increases in data throughput and link range without additional bandwidth or transmit power.
In recent years, many literatures advance the optimum algorithm for MIMO signal detection and sub-optimum algorithm for MIMO signal detection. The full-search-based detection is the optimum detection method and minimizes the bit error rate (BER). At the portion of sub-optimum algorithm, include the genetic algorithm (GA) and particle swarm optimization (PSO), by take advantage of gene evolution and the collective intelligence of swarms, the proposed detector offers a slightly degraded bit error rate performance compared with the full-search-based optimum detector does while greatly reducing the computation complexity.

誌謝 I
中文摘要 II
英文摘要 III
目錄 IV
圖目錄 VII
表目錄 IX
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 4
1.3 論文架構 6
二、 多重輸入多重輸出系統介紹 7
2.1 多重輸入多重輸出(MIMO)架構 8
2.2 通道模型(CHANNEL MODELING)與正規化(NORMALIZATION) 11
三、 傳統訊號偵測(TRADITIONAL SIGNAL DETECTION) 13
3.1 強制歸零(ZERO FORCING, ZF) 14
3.2 最小均方根誤差(MINIMUM MEAN SQUARE ERROR, MMSE) 16
3.3 貝爾實驗室垂直分層空時(VERTICAL BELL LABORATORIES SPACE TIME, V-BLAST) 18
3.4 最大相似度偵測法(MAXIMUM LIKELIHOOD, ML) 20
3.5 結論(CONCLUSION) 22
四、 利用啟發式演算法之訊號偵測 23
4.1 基因演算法(GENETIC ALGORITHM, GA) 23
4.1.1 基因演算法(GA)演算法原理 23
4.1.2 基因演算法(GA)流程介紹 24
4.2 粒子群聚最佳化(PARTICLE SWARM OPTIMIZATION, PSO)演算法 30
4.2.1 粒子群聚最佳化(PSO)演算法原理 30
4.2.2 粒子群聚最佳化(PSO)演算法流程介紹 31
4.3 基因群聚最佳化(GENETICAL SWARM OPTIMIZATION, GSO)演算法 34
五、 啟發式訊號偵測演算法應用於多重輸入多重輸出系統 36
5.1 利用基因演算法於多重輸入多重輸出系統訊號偵測 37
5.2 利用粒子群聚最佳化演算法於多重輸入多重輸出系統訊號偵測 42
5.3 利用基因群聚最佳化演算法於多重輸入多重輸出系統訊號偵測 46
六、 研究結果 50
6.1 衍生遺傳演算法之收斂速度比較 51
6.2 位元錯誤率分析比較 54
七、 結論與未來研究方向 58
7.1 結論 58
7.2 未來研究方向 60
參考文獻 61

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