臺灣博碩士論文加值系統

廣義空間調變 (Generalized Spatial Modulation)是一個新型的多輸入多輸出 (Multiple-Input Multiple-Output)技術，在每個時隙 (Time Slot)中僅會啟動數根天線，儘管最大概似 (Maximum Likelihood, ML)檢測器可以實現最佳性能，但窮舉搜尋會導致難以處理的運算複雜性。
本篇論文使用了一種名為A-star的搜尋演算法，其採用了一套特殊的啟發性函數 (Heuristic Function)，能將一些明顯較差的路徑直接排除，在不損失性能的情況下，還能有較低的複雜度，然而判斷每個節點的數值，我們則是用碼字輔助硬式檢測器 (Codebook-Assisted Hard Decision, CAHD)之成本函數，CAHD為樹狀搜尋的系統，藉由調整每層要保留的路徑數，創造出低複雜度的檢測器，但CAHD在算某個訊號時，是把其他的訊號及雜訊一同視為干擾，雖然擁有很低的複雜度，但高訊雜比 (Signal-to-Noise Ratio, SNR)時會出現錯誤平層 (Error Floor)。
我們所提出之演算法因為使用了A-star搜尋，能有效解決CAHD的錯誤平層，而模擬結果表明，我們不但能保有ML的性能，且複雜度還低於ML。此外，我們利用在不同SNR下使用不同權重，讓整體近似ML的性能，但複雜度能有更明顯的下降。

Generalized spatial modulation (GSM) is a novel multiple-input-multiple-out (MIMO) technique, in which only several transmit antennas are activated in each time slot. Although the maximum likelihood (ML) detector is able to achieve the optimal performance, exhaustive search leads to computational complexity that is difficult to handle. In this paper, a search method called A-star algorithm is adopted, which uses a special heuristic function to directly remove some obviously poor paths. It can have lower complexity without losing performance. However, to determine the value of each node, we use the cost function of codebook-assisted hard decision (CAHD), which is a tree search system. CAHD is a low-complexity detector by setting the number of paths to be reserved for each layer. But when a certain signal is calculated, the other signals and noise are regarded as interference. This has a low complexity, but there is an error floor at high SNR. Our proposed algorithm effectively solves error floor of CAHD because we use the A-star search algorithm. This simulation results show that we can maintain the performance of ML, and the complexity is still lower than ML. On the other hand, we use different weights at different SNR to propose performance near-ML, but the complexity can be more significantly reduced.

論文審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT iv
目錄 v
圖次 vii
表次 ix
第一章 導論 1
1.1 研究動機 3
1.2 論文架構 3
第二章 系統介紹 5
2.1 空間調變系統之基本架構 5
2.2 廣義空間調變系統之基本架構 5
第三章 廣義空間調變多輸入多輸出檢測器 8
3.1 最大概似檢測器 8
3.2 碼字輔助硬式檢測器 8
第四章 低複雜度最大概似檢測器 13
4.1 A-star 演算法解碼描述 13
4.2 低複雜度最大概似檢測器 14
4.2.1 啟發性函數之設計方法 18
4.2.2 低複雜近似最大概似檢測器 21
第五章 模擬結果與討論 22
5.1 廣義空間調變檢測器之錯誤率與複雜度比較 22
5.2 廣義空間調變檢測器之低複雜度近似最大概似檢測器 34
第六章 結論 40
參考文獻 41
中英對照表 46
縮寫對照表 49

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