研究生: |
吳俊賢 Wu, Jyun-Sian |
---|---|
論文名稱: |
排列調變於多輸入多輸出、正交分頻多工、開迴路通訊之傳輸演算法與基於耦合之效能分析 Permutation Modulation for Open-Loop, Multi-Input Multi-Output and Orthogonal Frequency-Division Multiplexing System, and Associated Performance Analyses Using Copulas |
指導教授: |
賴以威
Lai, I-Wei |
口試委員: |
王嘉斌
Wang, Chia-Pin 李佳翰 Lee, Chia-Han 賴以威 Lai, I-Wei |
口試日期: | 2022/12/12 |
學位類別: |
碩士 Master |
系所名稱: |
電機工程學系 Department of Electrical Engineering |
論文出版年: | 2022 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 79 |
中文關鍵詞: | 錯誤率分析 、耦合 、多輸入多輸出系統 、正交分頻多工 |
英文關鍵詞: | error rate analyses, copula, multi-input multi-output, orthogonal frequency division multiplexing |
研究方法: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202205650 |
論文種類: | 學術論文 |
相關次數: | 點閱:101 下載:7 |
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此篇研究提出運用於多輸入多輸出系統 (multi-input multi-output, MIMO) 的 傳輸演算法。傳統的空間調變 (spatial modulation, SM) 演算法對單一時間點的多天線傳輸演算法進行調變,但是各個傳輸時間點獨立調變。本篇研究中,我們 提出一演算法不僅將多天線傳輸進行設計,且對多個時間點進行共同調變。此傳輸演算法相較於傳統的SM提升一個維度,增加所有傳輸可能組合間的最小歐式距離,在相同訊號雜訊比時,提升效能約 3 - 6 dB。
此外,此篇論文亦將此技術推廣至正交分頻多工 (orthogonal frequency- division multiplexing, OFDM) 中。在 OFDM 中,此篇論文提出的演算法提升其效能約 3 - 5 dB。
除了傳輸演算法設計,本文亦對傳輸演算法進行理論分析。藉由耦合 (copulas) 得以分析在通道具有相依性時的演算法效能,使分析更符合實際應用場景。
This thesis focuses on the transmission algorithm design of a multi-input multi- output (MIMO) system. Compared to conventional spatial modulation (SM), which individually modulates the signal within one time instant, the proposed transmission algorithm utilizes permutation modulation to modulate multiple time instants concerning several system parameters. Since the proposed algorithm modulates multiple time instants, there is one more dimension than the conventional SM, increasing the minimum Euclidean distance within the set of all possible transmission signals.
This thesis utilizes permutation coding to design the transmission of orthogonal frequency division multiplexing (OFDM). The proposed transmissions increase by 3-5 dB for OFDM.
Besides the transmission algorithm design, we theoretically analyze the bits error rates of transmission algorithms. The copula can efficiently construct the multi-variable cumulative distribution function (CDF) with correlations.
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