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研究生:黃宥家
研究生(外文):You-chia Huang
論文名稱:低複雜度的廣義空時位移鍵入解碼演算法
論文名稱(外文):Low Complexity Decoding Algorithm for Generalized Space-Time Shift Keying
指導教授:黃婉甄黃婉甄引用關係
指導教授(外文):Wan-Jen Huang
學位類別:碩士
校院名稱:國立中山大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:47
中文關鍵詞:空時編碼巨量多輸入多輸出系統最大概似偵測法則空間移位鍵入技術
外文關鍵詞:space-time codingmaximum likelihood(ML)massive multi-input multi-output system(MMIMO)space shift keying
相關次數:
  • 被引用被引用:0
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  • 下載下載:6
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由於現代通訊系統對於頻譜效率的要求巨量多輸入多輸出(massive multiple-input multiple-output, MMIMO)系統近期受到不少關注,然而,如何在傳送時傳送的訊號不會受到通道之間的干擾和降低傳送端所需的RF鏈,是此系統實作時很大的挑戰,空間移位鍵入技術(space shift keying, SSK)是解決上述的方法之一,其重點在於利用天線序號去傳送額外的資料量,相比傳統的傳送方式,此技術額外利用了空間這個維度。然而,在空間移位鍵入技術之中,傳送端尚未利用多天線的空間多樣性,所以我們採用廣義空時編碼空間移位鍵入技術(generalized space-time shift keying, GSTSK)傳送訊號,結合空間移位鍵入技術及空時編碼的概念,以獲取空間多樣性,並且承襲空間移位鍵入技術的所有優點,利用調變訊號和天線序號去傳遞資料。此技術對應之最佳解碼方式為最大概似偵測法則(maximum likelihood,ML),雖然可利用正交空時編碼之正交的特性降低接收端的偵測複雜度,但是最大概似解碼法之複雜度仍正比於天線選擇的總數。特性在基地台天線數眾多時,解碼複雜度相當高,所消耗的能量和時間也相對比較高,為了降低複雜度,我們在本篇論文提出新的演算法,利用似然函數的加總結構,逐一找出基地台的傳送天線,以有效的降低解碼複雜度,減少能量和時間的消耗。在使用演算法一時,發現錯誤率較高,因此,為了解決這個問題,我們陸續找出演算法二和演算法三進而降低錯誤率,由於我們演算法,都是先個別找出天線在找符元,因此,在判斷上容易出錯造成在高訊雜比的時候仍會有一定比例的錯誤率。
As modern communication systems require a huge amount of spectral efficiency for massive multi-input multi-output system recently received a lot of attention.However, how the signal transmitted in the transmission will not be interference between the channels and the lower RF chain, this is a great challenge to implement in this system, space shift keying is one way to solve the above problems, the focus is to use an antenna to transmit additional information, compared to traditional delivery method, this technique takes advantage of the extra space dimension.However, in the space shift keying, the trassmitter unuses space diversity, so we use generalized space-time shift keying transmits the signals, which combined with space shift keying and space-time coding to get the space diversity,and inherited all the advantages of the space shift keying,and used the modulation and antenna number to transfer data.Corresponding to the optimum decoding for the maximum likelihood, although it can be used OSTBC orthogonal detection feature reduces the complexity of the receiving end, but the complexity of the maximum likelihood remain proportional to the total number of antenna selection. Its characteristic when the number of base station antennas, decoding complexity are very high, energy and time consumption is relatively high.In order to reduce complexity, we propose in this paper a new algorithm, using the likelihood function plus the total structure, identifying each base station transmitted antennas to effectively reduce the decoding complexity, reduce the consumption of energy and time.When using algorithm 1, but,there is a higher error rate, therefore, to solve this problem, we have to find the algorithm 2 and 3 algorithms thereby reducing the error rate, because our algorithms, are the first to identify the transmitted antenna and,the next to find the symbol, therefore, prone to errors in judgment have caused still in high SNR.
審定書....................................................................i
致謝.......................................................................ii
摘要......................................................................iii
Abstract................................................................iv
表索引.................................................................viii
圖索引...................................................................ix
第一章 簡介...........................................................1
第二章 文獻探討.....................................................5
2.1 正交空時編碼..................................................5
2.2 空間位移建入..................................................6
2.3 廣義的空間位移鍵入........................................8
第三章 系統模型..................................................11
第四章 正交空時編碼的解碼設計...........................13
4.1 正交空時編碼.................................................14
4.2 傳送端使用兩根天線的傳送的編碼....................16
4.3 最大四然法則之簡化.......................................19
4.4 低複雜度解碼演算法.......................................20
4.4.1解碼演算法一...........................................21
4.4.2解碼演算法二...........................................25
4.4.3解碼演算法三...........................................25
4.4.4解碼演算法問題........................................27
4.5 複雜度...........................................................28
第五章 模擬結果與討論...........................................32
5.1 解碼演算法一.................................................32
5.2 解碼演算法二.................................................33
5.3 解碼演算法三.................................................34
第六章 結論...........................................................37
參考文獻...............................................................38
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