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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇炫榮(Hsuan-Jung Su) | |
dc.contributor.author | Yi-Ting Hou | en |
dc.contributor.author | 侯亦庭 | zh_TW |
dc.date.accessioned | 2021-06-16T17:53:55Z | - |
dc.date.available | 2020-03-03 | |
dc.date.copyright | 2020-03-03 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-02-26 | |
dc.identifier.citation | [1] O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE transactions on wireless communications, vol. 13, no. 3, pp. 1499–1513, 2014.
[2] F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale MIMO systems,” in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015, pp. 2929–2933. [3] A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel estimation and hybrid precoding for millimeter wave cellular systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5, pp. 831–846, 2014. [4] A. Alkhateeb, G. Leus, and R. W. Heath, “Limited feedback hybrid precoding for multi-user millimeter wave systems,” IEEE transactions on wireless communications, vol. 14, no. 11, pp. 6481–6494, 2015. [5] C.-R. Tsai, Y.-H. Liu, and A.-Y. Wu, “Efficient compressive channel estimation for millimeter-wave large-scale antenna systems,” IEEE Transactions on Signal Processing, vol. 66, no. 9, pp. 2414–2428, 2018. [6] A. Mezghani and L. Swindlehurst, “Blind estimation of sparse multiuser massive MIMO channels,” in WSA 2017; 21th International ITG Workshop on Smart Antennas. VDE, 2017, pp. 1–5. [7] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE transactions on antennas and propagation, vol. 34, no. 3, pp. 276–280, 1986. [8] P. Pal and P. P. Vaidyanathan, “Nested arrays: A novel approach to array processing with enhanced degrees of freedom,” IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 4167–4181, 2010. [9] T.-J. Shan, M. Wax, and T. Kailath, “On spatial smoothing for direction-of-arrival estimation of coherent signals,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 33, no. 4, pp. 806–811, 1985. [10] P. Pal and P. Vaidyanathan, “Correlation-aware techniques for sparse support recovery,” in 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012, pp. 53–56. [11] A. Maleki, L. Anitori, Z. Yang, and R. G. Baraniuk, “Asymptotic analysis of complex lasso via complex approximate message passing (CAMP),” IEEE Transactions on Information Theory, vol. 59, no. 7, pp. 4290–4308, 2013. [12] D. Neumann, M. Joham, and W. Utschick, “Channel estimation in massive MIMO systems,” arXiv preprint arXiv:1503.08691, 2015. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64549 | - |
dc.description.abstract | 毫米波為第五代行動通訊中一重要技術,它使我們有更高的資料傳輸率以及更大的可使用頻寬。然而,由於毫米波的超高頻率特性使其受限於極高的途徑損失以及比起傳統無線通訊更少的路徑數目。熱門的解決辦法為採用大規模多輸入多輸出及波束形成技術。而我們需要通道狀態資訊才能利用數種已存的電波束形成器及結合器設計演算法。在絕大多數的通道估測研究中,研究者們傾向採用領航序列來增加估測的準確性,但這會降低資料傳輸率。因此我們試圖提出一個可以避免使用領航序列以及維持可接受的估測準確率的新穎方法。我們進一步地將這個問題分為兩種情況。第一種情況是單一路徑,也就是每一個使用者到基地台的通道都為只有一個。多訊號分類演算法以及巢狀陣列將被用於此情況。而另一種情況則為多路徑。我們利用壓縮感知以及巢狀陣列來估測通道。 | zh_TW |
dc.description.abstract | Millimeter wave (mmWave) cellular communication system is a promising technology in the fifth-generation communication. Because of the ultra-high frequency, it suffers from forbiddingly high path loss and the paths from users to the base station (BS) are much fewer than the traditional wireless communications systems. It is popular to exploit massive multi-input multi-output (MIMO) systems and beamforming techniques to deal with this problem. The pilot-based channel estimation algorithms become more inefficient when the number of antennas increases. Therefore, we propose a novel method that avoids using the pilot sequence. We first estimate the user number by the rank of the correlation matrix of the received signals. Then, the compressed sensing technique is utilized to estimate the direction-of-arrival (DOA) of each path. Eventually, we take advantage of the special structure of the fading matrix to eliminate the spurious DOAs and estimate the fading gain of each path at the same time. A special case that the available path from each user to the base station (BS) is one is also discussed, and we exploit multiple signal classification (MUSIC) algorithm to reduce complexity. In both cases, a non-uniform antenna array called the nested array is utilized to increase the array aperture. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:53:55Z (GMT). No. of bitstreams: 1 ntu-109-R06942096-1.pdf: 859548 bytes, checksum: 5fc1761697d42cd070e86a249ad2a117 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 System Model and Problem Formulation 6 2.1 MUSIC Algorithm and Spatial Smoothing Technique . . . . . 6 2.1.1 DOA Estimation with Uncorrelated Users . . . . . . . 7 2.1.2 DOA estimation problem with correlated users . . . . . 8 2.2 Nested Array and the DOA Estimation . . . . . . . . . . . . . 9 2.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4 Problem Description . . . . . . . . . . . . . . . . . . . . . . . 13 3 Proposed Method for Blind Millimeter Channel Estimation 14 3.1 Blind Millimeter Wave Channel estimation for Multi-path Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 User Number Estimation . . . . . . . . . . . . . . . . . 15 3.1.2 Direction-Of-Arrival and Fading Gain Estimation . . . 17 3.1.3 The Effect of Phase Ambiguity . . . . . . . . . . . . . 20 3.2 Blind Millimeter Wave Channel estimation for Single Path Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2.1 Direction of Arrival Estimation Using the Nested Array and MUSIC Algorithm . . . . . . . . . . . . . . . . . . 22 3.2.2 Fading Gain Estimation Using the Nested Array and MUSIC Algorithm . . . . . . . . . . . . . . . . . . . . 24 3.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . 25 4 Simulation Results 4.1 Performance Metric . . . . . . . . . . . . . . . . . . . . . . . . 26 4.2 Multi-path Scenario . . . . . . . . . . . . . . . . . . . . . . . . 27 4.3 Single Path Scenario . . . . . . . . . . . . . . . . . . . . . . . 29 5 Conclusions 32 Bibliography 33 | |
dc.language.iso | en | |
dc.title | 藉由多重訊號分類演算法及壓縮感知做盲通道估測 | zh_TW |
dc.title | Blind Channel Estimation via MUSIC Algorithm and Compressed Sensing | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林澤(Che Lin),劉俊麟(Chun-Lin Liu) | |
dc.subject.keyword | 大規模多輸入多輸出,毫米波,巢狀陣列,多訊號分類演算法,通道估測,壓縮感知, | zh_TW |
dc.subject.keyword | Massive MIMO,Millimeter Wave,Nested Array,MUSIC Algorithm,Channel Estimation,Compressed Sensing, | en |
dc.relation.page | 35 | |
dc.identifier.doi | 10.6342/NTU202000601 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-02-27 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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