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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇炫榮 | |
dc.contributor.author | Hsiang-Yu Liu | en |
dc.contributor.author | 劉翔瑜 | zh_TW |
dc.date.accessioned | 2021-07-11T14:55:48Z | - |
dc.date.available | 2022-04-28 | |
dc.date.copyright | 2020-05-21 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2020-04-21 | |
dc.identifier.citation | [1] Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A.Li, and K. Higuchi, Non-orthogonal multiple access (noma) for cellular future radio access,' in 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), June 2013, pp. 1-5. [2] R. Hoshyar, R. Razavi, and M. Al-Imari, Lds-ofdm an efficient multiple access technique,' in 2010 IEEE 71st Vehicular Technology Conference, May 2010, pp. 1-5. [3] H. Nikopour and H. Baligh, 'Sparse code multiple access,' in 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2013, pp. 332-336. [4] M. Taherzadeh, H. Nikopour, A. Bayesteh, and H. Baligh, 'Scma codebook design,' in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall). IEEE, 2014, pp. 1-5. [5] M. Kim, N. Kim, W. Lee, and D. Cho, 'Deep learning-aided scma,' IEEE Communications Letters, vol. 22, no. 4, pp.720-723, April 2018. [6] K. He, X. Zhang, S. Ren, and J. Sun, 'Delving deep into rectifiers:Surpassing human-level performance on imagenet classification,' CoRR,vol. abs/1502.01852, 2015. [Online]. Available: http://arxiv.org/abs/1502.01852 [7] F. Liang, C. Shen, and F. Wu, 'An iterative bp-cnn architecture for channel decoding,' IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 144-159, Feb 2018. [8] T. Gruber, S. Cammerer, J. Hoydis, and S. t. Brink, 'On deep learning-based channel decoding,' in 2017 51st Annual Conference on Information Sciences and Systems (CISS), March 2017, pp. 1-6. [9] S. Cammerer, T. Gruber, J. Hoydis, and S. ten Brink, 'Scaling deep learning-based decoding of polar codes via partitioning,' in GLOBECOM 2017 - 2017 IEEE Global communications Conference, Dec 2017, pp. 1-6. [10] L. Lugosch and W. J. Gross, 'Neural offset min-sum decoding,' in 2017 IEEE International Symposium on information Theory (ISIT), June 2017, pp. 1361-1365. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78415 | - |
dc.description.abstract | 為了處理當今各種通訊應用對於極高傳輸速度的要求,非正交多重存取這項概念 在第五代行動通訊受到了極大的關注,這個概念大幅增加了頻譜效率。在近期的 研究中,其中一種非正交多重存取技術-稀疏碼多重存取,取得了出眾的表現, 然而,這項技術表現出色的關鍵在於碼書的建立,若要連同現實中出現的各種環 境因素一併考慮的話,碼書的設計將會變得複雜且困難。因此,我們尋求了深度 學習的方法,其中一種就是利用自編碼架構與通訊模型相似的特性,將其套入我 們的系統中,訓練出區域最佳解的碼書及解碼器。在這篇論文中,我們將會更進 一步探討這個架構,並提出幾種簡化模型的想法,於模擬結果中能發現我們的簡 化不僅降低計算量,也達到了一樣低的錯誤率。在論文的最後,我們簡化了在編 碼器中的深度神經模型,將其用一個線性層取代,發現其仍然能達到相同的表 現。從這個奇特的結果,我們推斷由深度模型組成的編碼器(碼書),在訓練的過 程中並沒有好好的利用到非線性的淺力,我們需要重新思考更適合這類通訊系統 的深度架構或是訓練方式。 | zh_TW |
dc.description.abstract | To cope with the excessive data rate demands of future multimedia ap- plications, nonorthogonal multiple access (NOMA) techniques has drawn sig- ni ciant interests in improving the spectral e eiciency (SE). Recently, sparse code multiple access (SCMA), one of the NOMA schemes, achieves outstand- ing performance. However, the SCMA performance highly depends on the condebook construction, and it is di ucult to construct an optimal codebook for di erent situation in a handcrafted manner. To address this task, there has been some solutions with the help of deep learning. One of the solutions is constructing the encoder codebook and the decoder with the autoencoder structure. In this thesis, we make the performance of autoencoder superior to that of handcrafted codebook by applying some methods and ne tuning the hyperparameters. Then we propose two schemes, the lookup table based encoder and two-stage encoder. The lookup table based encoder not only highly reduces the computational complexity during training but achieves slightly better performance. The two-stage encoder is designed for the situ- ation where there is large number of symbol combinations of di erent users, and it converges more rapidly comparing to the lookup table based encoder. Finally, based on our results, we simplify the deep neuron network (DNN) based encoder of the autoencoder model to a linear operation, and nding that it also has the similar performances with the DNN based encoder. From this peculiar result, we conjecture that the DNN encoder may not exploit the non-linear capacity well, we must explore some other particular structures to suit the NOMA system. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:55:48Z (GMT). No. of bitstreams: 1 ntu-108-R06942095-1.pdf: 2356022 bytes, checksum: e8a3e831a40d2399834e05ccdd17b4e1 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 1 Introduction 1 1.1 Backround . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of Thesis . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Introduction to OMA, NOMA and SCMA . . . . . . . . . . . 4 2 System Model And Description 8 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Autoencoder and SCMA codebook . . . . . . . . . . . . . . . 10 3 Construction of NOMA codebook with Deep Learning 12 3.1 Training details . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Lookup Table based Encoder 20 4.1 Simpli cation with Lookup Table . . . . . . . . . . . . . . . . 20 4.2 Increasing the Block Length . . . . . . . . . . . . . . . . . . . 25 4.2.1 Previous Work . . . . . . . . . . . . . . . . . . . . . . 26 5 Two stage encoder 28 5.1 The ability of linear function and nonliear function . . . . . . 33 6 Simulations and Settings 36 7 Conclusion 40 Bibliography 41 | |
dc.language.iso | en | |
dc.title | 藉由深度學習方法找出非正交多重存取之碼書 | zh_TW |
dc.title | Finding NOMA Codebook with Deep Learning-Aided Approaches | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李宏毅,林澤 | |
dc.subject.keyword | 非正交多重存取,深度學習,稀疏碼多重存取,自編碼器,碼書建立,深度神經網路, | zh_TW |
dc.subject.keyword | Nonorthogonal Multiple Access,Deep Learning,Sparse Code Multiple Access,Autoencoder,Codebook Constuction,Deep Neural Net work, | en |
dc.relation.page | 42 | |
dc.identifier.doi | 10.6342/NTU202000746 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-04-21 | |
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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