因應未來行動通訊對功率效率、頻譜效率、安全及可靠度的更高要求，大規模多天線系統被視為能達到此要求的架構。儘管大規模多天線無線通信技術具有潛力，但巨量天線陣列也伴隨著解調器的複雜度提高。本論文利用近似訊息傳遞演算法對大規模多天線系統的基礎問題提出通用的解決方案，將涵蓋一項議題： 1) 開發低複雜度的訊號解調技術。為了之後能在硬體實現，本論文更進一步探討近似訊息傳遞解調器的定點量化。這發展預計形成適用於下世代行動通訊的新型無線傳輸理論，並完成關鍵技術的仿真驗證。

Massive MIMO systems are widely considered as a future cellular network architecture, which are anticipated to be energy-efficient, spectrum-efficient, secure, and robust. Despite the potential, massive MIMO systems cause a substantial increase in computational complexity for signal detection.
In this thesis, we provide a universal solution to the massive MIMO system by the approximate message passing algorithm. The developed solutions can efficiently deal with the high complexity problem of signal demodulation in massive MIMO system. In addition, we analyze the effect of fixed point quantization for hardware implementation of approximate message passing algorithm. It is anticipated to achieve systematical results in the theory and technique of massive MIMO wireless communication.

論文審定書 i
誌謝 ii
中文摘要 iii
英文摘要 iv
目錄 v
圖次 vii
表次 x
1 緒論 1
1.1 前言 1
1.2 論文章節架構 3
2 背景與文獻調查 4
2.1 研究背景 4
2.2 近似訊息傳遞演算法歷史 6
3 系統模型及訊號解調 7
3.1 系統模型 7
3.2 訊號解調 8
3.3 逼零解調器 9
3.4 線性最小均方誤差解調器 10
3.5 最大可能性解調器 10
3.6 近似訊息傳遞解調器 11
3.6.1 Belief Propagation (BP) 演算法 13
3.6.2 Relaxed Belief Propagation (rBP) 演算法 15
3.6.3 Approximate Message Passing (AMP) 演算法 16
4 模擬及討論 20
4.1 大規模多天線系統架構 20
4.2 近似訊息傳遞解調器與線性解調器之比較 20
4.2.1 在基地站天線個數 N 與終端裝置個數 M 相等之情況下 20
4.2.2 在基地站天線個數 N 為終端裝置個數 M 兩倍之情況下 26
4.2.3 在基地站天線個數 N 為終端裝置個數 M 四倍之情況下 35
4.2.4 在基地站天線個數 N 為終端裝置個數 M 八倍之情況下 47
4.2.5 複雜度分析 53
4.3 近似訊息傳遞解調器之定點量化 54
4.3.1 量化無損耗之情況下 55
4.3.2 量化損耗 0.1 分貝之情況下 57
5 結論 59
參考文獻 60
附錄A 62
附錄B 68

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