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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82067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 | Sheng-Chen Wu | en |
| dc.contributor.author | 吳聲辰 | zh_TW |
| dc.date.accessioned | 2022-11-25T05:35:07Z | - |
| dc.date.available | 2024-01-01 | |
| dc.date.copyright | 2022-02-17 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-01-06 | |
| dc.identifier.citation | M. B. Shahab, R. Abbas, M. Shirvanimoghaddam and S. J. Johnson, 'Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey,' in IEEE Communications Surveys Tutorials, vol. 22, no. 3, pp. 1805-1838, thirdquarter 2020. L. Liu, E. G. Larsson, W. Yu, P. Popovski, C. Stefanovic and E. de Carvalho, 'Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things,' in IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 88-99, Sept. 2018. W. Yu, 'On the fundamental limits of massive connectivity,' 2017 Information Theory and Applications Workshop (ITA), 2017, pp. 1-6. B. Wang, L. Dai, Y. Yuan and Z. Wang, 'Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Non-Orthogonal Multiple Access,' 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), 2015, pp. 1-5. Z. Chen, F. Sohrabi and W. Yu, 'Sparse Activity Detection for Massive Connectivity,' in IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1890-1904, 1 April1, 2018. K. Senel and E. G. Larsson, 'Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach,' in IEEE Transactions on Communications, vol. 66, no. 12, pp. 6164-6175, Dec. 2018. T. Hara and K. Ishibashi, 'Grant-Free Non-Orthogonal Multiple Access With Multiple-Antenna Base Station and Its Efficient Receiver Design,' in IEEE Access, vol. 7, pp. 175717-175726, 2019. S. Foucart and H. Rauhut, “An invitation to compressive sensing,” in A mathematical introduction to compressive sensing. Springer, 2013, pp. 1–39. D. Needell and J. A. Tropp, “Cosamp: Iterative signal recovery from incomplete and inaccurate samples,” Applied and computational harmonic analysis, vol. 26, no. 3, pp. 301–321, 2009. A. Maleki, “Approximate message passing algorithms for compressed sensing,” Ph.D. dissertation, Stanford University, 2010. D. L. Donoho, A. Maleki, and A. Montanari, “Message-passing algorithms for compressed sensing,” Proceedings of the National Academy of Sciences, vol. 106, no. 45, pp. 18 914–18 919, 2009. J. Ziniel and P. Schniter, “Efficient high-dimensional inference in the multiple measurement vector problem,” IEEE Transactions on Signal Processing, vol. 61, no. 2, pp. 340–354, 2012. M. Mishali and Y. C. Eldar, “Reduce and boost: Recovering arbitrary sets of jointly sparse vectors,” IEEE Transactions on Signal Processing, vol. 56, no. 10, pp. 4692–4702, 2008. C.-T. Liu, H.-J. Su, and Y. Takano, “Sparse activity, timing detection and channel estimation for grant-free uplink communications,” in 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2020, pp. 1–7. M. Bayati and A. Montanari, “The dynamics of message passing on dense graphs, with applications to compressed sensing,” IEEE Transactions on Information Theory, vol. 57, no. 2, pp. 764–785, 2011. 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. J. Chen and X. Huo, “Theoretical results on sparse representations of multiple-measurement vectors,” IEEE Transactions on Signal processing, vol. 54, no. 12, pp. 4634–4643, 2006. S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2477–2488, 2005. X. Meng, S. Wu, L. Kuang, and J. Lu, “An expectation propagation perspective on approximate message passing,” IEEE Signal Processing Letters, vol. 22, no. 8, pp. 1194–1197, 2015. J. Kim, W. Chang, B. Jung, D. Baron, and J. C. Ye, “Belief propagation for joint sparse recovery,” arXiv preprint arXiv:1102.3289, 2011. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82067 | - |
| dc.description.abstract | 第五代無線行動通訊中，巨量多機器型態通訊支援大量物聯網設備。這些物聯網設備會零星地處於活動狀態，並且向基地台發送數據。為了處理這個龐大的通訊系統，免授權非正交多址已經大量被使用。由於零星連接的流量模式，用戶檢測和通道估測可以被制定為壓縮感知問題。因為基地台有多根天線，問題變成了多測量向量問題。對於實際情況，非同步系統需要被考慮。因此，我們利用近似消息傳遞演算法來檢測用戶活動並估計通道和定時偏移。我們利用近似消息傳遞演算法的特性通過狀態演化來預測近似消息傳遞演算法的性能。為了降低複雜度，我們提出平行減少多測量向量和促進近似消息傳遞演算法，此演算法將減少多測量向量和促進演算法與平行近似消息傳遞演算法相結合。模擬結果表明，狀態演化可以很好地預測虛警機率和漏檢機率。此外，平行減少多測量向量和促進近似消息傳遞演算法在恢復稀疏矩陣方面具有很高的性能。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T05:35:07Z (GMT). No. of bitstreams: 1 U0001-0501202220584500.pdf: 1671547 bytes, checksum: 910da312d71f08bfdb2d726ba88345bc (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Contents 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of the Thesis . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 System Model and Problem Formulation 6 2.1 Compressed Sensing . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Approximate Message Passing Algorithm . . . . . . . . 8 2.1.2 Reduce MMV and Boost Algorithm . . . . . . . . . . . 10 2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Problem Description . . . . . . . . . . . . . . . . . . . . . . . 14 3 AMP Algorithm for Detection and Estimation 15 3.1 AMP-MMV . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 Parallel AMP-MMV . . . . . . . . . . . . . . . . . . . . . . . 20 3.3 State Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4 Proposed Method with Low Complexity 25 4.1 ReMBo AMP . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2 Parallel ReMBo AMP . . . . . . . . . . . . . . . . . . . . . . 27 4.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . 30 5 Simulation results 32 5.1 Simulation Parameters and Metrics . . . . . . . . . . . . . . . 32 5.2 AMP-based Algorithm for Detection . . . . . . . . . . . . . . 33 5.3 Low Complexity Method for Detection . . . . . . . . . . . . . 38 6 Conclusions 45 Bibliography 46 A The Vector Denoiser Function in Virtual System 50 B MMV Form of the Onsager Reaction Term 51 C False Alarm Probability and Miss Detection Probability 54 | |
| dc.language.iso | en | |
| dc.subject | 壓縮感知 | zh_TW |
| dc.subject | 近似消息傳遞演算法 | zh_TW |
| dc.subject | 免授權 | zh_TW |
| dc.subject | 非正交多址 | zh_TW |
| dc.subject | 減少多測量向量和促進演算法 | zh_TW |
| dc.subject | Reduce MMV and Boost | en |
| dc.subject | Grant Free | en |
| dc.subject | Non-orthogonal Multiple Access | en |
| dc.subject | Compressed Sensing | en |
| dc.subject | Approximate Message Passing | en |
| dc.title | 在非同步上行系統藉由近似消息傳遞演算法進行檢測與估計 | zh_TW |
| dc.title | Detection and Estimation via Approximate Message Passing Algorithm in the Asynchronous Uplink System | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林士駿(Hsin-Tsai Liu),林茂昭(Chih-Yang Tseng) | |
| dc.subject.keyword | 免授權,非正交多址,壓縮感知,近似消息傳遞演算法,減少多測量向量和促進演算法, | zh_TW |
| dc.subject.keyword | Grant Free,Non-orthogonal Multiple Access,Compressed Sensing,Approximate Message Passing,Reduce MMV and Boost, | en |
| dc.relation.page | 55 | |
| dc.identifier.doi | 10.6342/NTU202200015 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-01-07 | |
| 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 |
| dc.date.embargo-lift | 2024-01-01 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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