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姓名 周書安(Su-An, Chou) 查詢紙本館藏 畢業系所 電機工程學系 論文名稱 應用於多使用者多輸入多輸出混合式波束合成系統之非均勻可調式特徵值解析器設計
(Design of A Unified and Flexible Eigen-Solver for Hybrid Beamforming Algorithm in Multi-User MIMO Systems)相關論文 檔案 [Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 (2025-7-22以後開放) 摘要(中) 多使用者多輸入多輸出混合式波束合成系統為5G通訊的重要特色,由於此通訊系統的基站(base station)天線數量很高,因此在效能與運算複雜度之間折衷是多使用者多輸入多輸出系統能廣泛佈建的重要考量,需利用盡量少的資源消耗與運作成本來達成使用者所需的效能。
本論文透過碼簿(codebook)配合階層式搜尋法(hierarchy search),減少波束搜尋(beam search)的時間複雜度,並比較各式碼簿配合搜尋法的效能與位元錯誤率(bit error rate)後,最後決定使用Discrete Fourier Transform (DFT)碼簿配合Multipath Decomposition and Recovery (MDR)搜尋法進行波束搜尋來實現射頻端的相移器設定,以避免射頻鏈(RF chain)數量過多,並且能夠擁有良好的效能及低位元錯誤率。
特徵值解析器則於多使用者多輸入多輸出系統中扮演重要的角色,零特徵值之特徵向量可避免其他使用者的干擾(interference),而最大的特徵值之特徵向量可以確保訊號傳輸的過程中擁有最大的增益(gain)。本論文所提出的非均勻可調式特徵值解析器能夠令使用者調整參數,在計算複雜度與效能間得到適當的折衷效果。我們使用零特徵值模式設計發送端數位部分的前編器,並使用非零特徵值模式設計接收端的組合器(combiner)。本論文最後以40奈米製程實作非均勻可調式特徵值解析器之硬體。此硬體的處理器一可以支援 3×4 維度的輸入矩陣的零特徵值模式,而處理器二可以支援 5×5 維度的輸入矩陣運算非零特徵值模式吞吐量達到每秒5.26M (Mat per user/sec),gate count為472K,在時脈為5 ns,電壓為0.9 V的情況下,總功耗為215 mW摘要(英) Multi-user multiple input multiple output (MU-MIMO) hybrid beamforming is an important feature in 5G wireless communication systems. Because the number of transmit antennas at base station is large, the trade-off between performance and computational complexity is an important consideration.
We use DFT codebook and hierarchy search based on multipath decomposition and recovery to reduce timing complexity for beam search to derive the proper settings of phase shifts. At the mobile station, the antenna weight vector is obtained from eigen-vectors with large eigenvalues to achieve good capacity.
Eigen-solvers plays an important role in MU-MIMO system. The eigenvectors associated with the zero eigenvalues are needed since it can be used to get rid of the interference. On the other hand, the eigenvectors corresponding to the largest eigenvalues are often desired because they provide the subspaces with stronger gains. We propose a unified and flexible eigen-solver, which can reduce the computation complexity by adjusting the parameter for deflation. The digital precoders is realized by zero eigen-value mode, and the digital combiner is designed by non-zero eigen-value mode. Finally, the unified and flexible eigen-solver is implemented in CMOS 40nm technology. It can support 3×4 input matrix dimension in EVD processor 1 for zero-eigenvalue mode and 5×5 input matrix dimension in EVD processor 2 for non-zero eigen-value mode. The throughput is 5.26M (Mat per user/sec), and its gate count is 472K. The power consumption is 215mW at 0.9V and 200MHz operating frequency.關鍵字(中) ★ 波束合成 關鍵字(英) 論文目次 摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 簡介 1
1.2 研究動機 1
1.3 論文組織 2
第二章 多使用者多輸入多輸出系統下之混合式波束合成演算法 3
2.1 多使用者多輸入多輸出系統 3
2.2 系統環境 4
2.3 量化與無量化移相器之混合式波束合成演算法 6
第三章 波束搜尋(Beam Search) 10
3.1 碼簿與波束搜尋介紹 10
3.1.1 Beam Steering碼簿 (BSC) 10
3.1.2 Discrete Fourier Transform (DFT)碼簿 11
3.1.3 比較BSC與DFT碼簿 11
3.2 搜尋法介紹 14
3.2.1 窮盡搜尋法 14
3.2.2 階層式搜尋法 15
3.3 碼簿及搜尋法之效能及複雜度比較 18
第四章 非均勻可調式特徵解析器 26
4.1 均勻特徵解析演算法 26
4.1.1 零特徵值模式 26
4.1.2 非零特徵值模式 27
4.2 非均勻可調式特徵解析演算法 28
4.2.1 演算法介紹 29
4.2.2 非均勻可調式特徵解析演算法效能及複雜度模擬 30
第五章 非均勻可調式特徵解析器系統與實現 37
5.1 硬體關鍵運算 37
5.1.1 CORDIC相消旋轉模式 38
5.1.2 CORDIC角度旋轉模式 39
5.1.3 CORDIC架構 40
5.2 硬體架構 42
5.2.1 零特徵值運算模組 44
5.2.2 最大特徵值運算模組 51
5.3 硬體相關設計參數 60
5.4 硬體驗證與其他相關研究之硬體列表 66
5.4.1 硬體驗證 66
5.4.2 硬體與其他相關研究列表 73
第六章 結論 75
參考文獻 76參考文獻 [1] Xiao-Sheng Huang, “Design of Hybrid Beamforming Algorithm and Architecture for Multi-User MIMO Systems,” in master thesis, NCU, Taiwan, 2018, pp.1-60.
[2] S. Noh, M. D. Zoltowski and D. J. Love, "Multi-Resolution Codebook and Adaptive Beamforming Sequence Design for Millimeter Wave Beam Alignment," IEEE Transactions on Wireless Communications, vol. 16, no. 9, Sept. 2017, pp. 5689-5701.
[3] Z. Xiao, H. Dong, L. Bai, P. Xia and X. Xia, "Enhanced Channel Estimation and Codebook Design for Millimeter-Wave Communication," in IEEE Transactions on Vehicular Technology, vol. 67, no. 10, Oct. 2018, pp. 9393-9405.
[4] W. Wu, D. Liu, Z. Li, X. Hou and M. Liu, "Two-Stage 3D Codebook Design and Beam Training for Millimeter-Wave Massive MIMO Systems," 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, 2017, pp. 1-7.
[5] Y. C. Wu and P. Y. Tsai, “A generalized matrix-decomposition processor for joint MIMO transceiver design,” in Proc. of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, 2017, pp. 1153–1157.
[6] G. H. Golub and C. F. V. Loan, Matrix Computations (4th Edition), Baltimore, Maryland: The John Hopkins University Press, 2013.
[7] S. Chou, A. E. Rakhmania and P. Tsai, "A Unified and Flexible Eigen-Solver for Rank-Deficient Matrix in MIMO Precoding/Beamforming Applications," 2019 IEEE International Workshop on Signal Processing Systems (SiPS), Nanjing, China, 2019, pp. 218-223, doi: 10.1109/SiPS47522.2019.9020368.
[8] J. Guerrero-Ramirez, J. Velasco-Medina and J. Arce-Clavijo, “Hardware design of an eigensolver based on the QR method,” in IEEE Fourth Latin American Symposium on Circuits and Systems (LASCAS), Cusco, 2013.
[9] C. Chen, Z. Yang, C. Chen and Y. Huang, "A generalized eigenvalue decomposition processor for multi-user MIMO precoding,"2016 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Jeju, 2016, pp. 281-284, doi: 10.1109/APCCAS.2016.7803954.指導教授 蔡佩芸(Pei-Yun, Tsai) 審核日期 2020-7-24 推文 facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu 網路書籤 Google bookmarks del.icio.us hemidemi myshare