博碩士論文 107523031 詳細資訊



以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：12 、訪客IP：3.144.233.150
姓名 周彥丞(Yen-Cheng Chou)  查詢紙本館藏   畢業系所 通訊工程學系
論文名稱
(DRL-Based Adaptive Algorithm Selection for Massive MIMO Resource Allocation)
相關論文
★ 基於馬賽克特性之低失真實體電路佈局保密技術★ 多路徑傳輸控制協定下從無線區域網路到行動網路之無縫換手
★ 感知網路下具預算限制之異質性子頻段分配★ 下行服務品質排程在多天線傳輸環境下的效能評估
★ 多路徑傳輸控制協定下之整合型壅塞及路徑控制★ Opportunistic Scheduling for Multicast over Wireless Networks
★ 適用多用戶多輸出輸入系統之低複雜度比例公平性排程設計★ 利用混合式天線分配之 LTE 異質網路 UE 與 MIMO 模式選擇
★ 基於有限預算標價式拍賣之異質性頻譜分配方法★ 適用於 MTC 裝置 ID 共享情境之排程式分群方法
★ Efficient Two-Way Vertical Handover with Multipath TCP★ 多路徑傳輸控制協定下可亂序傳輸之壅塞及排程控制
★ 移動網路下適用於閘道重置之群體換手機制★ 使用率能小型基地台之拍賣是行動數據分流方法
★ 高速鐵路環境下之通道預測暨比例公平性排程設計★ 用於行動網路效能評估之混合式物聯網流量產生器
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中) 大規模多輸入多輸出天線陣列已經被視為一個在第五代通訊系統中新興的解決方案。大規模多輸入多輸出天線陣列透過在傳送端使用大量的服務天線以及操作在時分多工上，突破了當前實現上的限制。額外的天線有助於將能量集中在比較小的空間區域，從而極大的提高吞吐量及傳輸效能。然而在未來5G通訊中，資料流量將迅速的成長，特別是超高品質的多媒體，使得5G通訊中資源分配系統的效能越來越重要。隨著人工智慧的發展，也越來越多的研究使用機器學習來解決通訊網路的問題。因此，在本文我們提出了一種基於深度強化學習(DRL) 模型的適應性算法選擇架構，擴展成為解決在大規模多輸入多輸出天線陣列環境中多視角影片(MVV)的天線分配及影像合成(AAVS)問題的UMVS方法。在新的架構中，結合了兩種特殊的方法來輔助原本的UMVS方法，使得分配系統能表現得更全面。
此外，為了整合大規模多輸入多輸出天線陣列的資源分配問題，本文還提出一個結合使用者排程和混合預編碼的問題，並使用深度強化學習模型來解決。除了使用深度強化學習模型，我們還將在深度強化學習模型中的動作(Action)設計為組件化的動作(Componentized Action)，使得資源分配系統更加的靈活。
摘要(英) Massive multiple-input multiple-output (MIMO) is one of the essential technologies for 5G networks and beyond. At the same time, the traffic demand grows rapidly, largely due to emerging high-quality multimedia applications, and requires next-generation resource management approaches. In this work, we propose adaptive models selecting MIMO resource allocation methods with Deep Reinforcement Learning (DRL) on Multi-View video (MVV) multicasting and cross-layer resource allocation problems. Based on the Utility-based Multi-View Synthesis (UMVS) algorithm with theoretically-proven performance, the proposed solution adaptively complements UMVS with other representative methods. Besides, to integrate the resource allocation problem across protocol layers, the joint user scheduling and hybrid precoding problem also investigated with a componentized action model, including fundamental allocation methods. Simulations show more users in the MIMO system are satisfied under DRL-based allocation schemes.
關鍵字(中) ★ 強化學習
★ 資源分配
★ 多天線陣列
★ 結合排程及預編碼
★ 多視角影片應用		 關鍵字(英)
論文目次 1 Introduction......................................... 1
1.1 Background........................................ 1
1.2 Motivation........................................ 1
1.3 Contribution...................................... 2
1.4 Framework......................................... 3
2 Background and Related Works......................... 4
2.1 Multi-View Video and Depth Image-Based Rendering.. 4
2.2 Precoding in Massive Multi-Input Multi-Output..... 4
2.3 Joint Scheduling and Precoding in Massive MIMO.... 5
2.4 Resource Management with Machine Learning......... 5
2.5 Deep Reinforcement Learning and DDPG.............. 6
3 DDPG-Based Adaptive Antenna Allocation Extension for UMVS................................................... 8
3.1 System Model and Problem Formulation.............. 8
3.1.1 System Model................................... 8
3.1.2 Problem Formulation........................... 10
3.1.3 Multi-View Synthesis Utility Function......... 11
3.2 Extension on Deep Deterministic Policy Gradient.. 12
3.2.1 Markov Decision Process Formulation........... 13
3.3 Performance Evaluation........................... 14
3.3.1 DDPG Setup.................................... 14
3.3.2 Simulation Setup.............................. 14
3.3.3 Simulation Results............................ 15
4 Joint Scheduling and Precoding in Massive MIMO System via Deep Reinforcement Learning....................... 20
4.1 System Model and Problem Formulation............. 20
4.1.1 User Scheduling Problem....................... 21
4.1.2 Hybrid Precoding Problem...................... 22
4.1.3 Joint Scheduling and Precoding Problem Formulation........................................... 23
4.2 Deep Reinforcement Learning for Massive MIMO Resource Allocation................................... 24
4.2.1 Markov Decision Process Formulation........... 24
4.2.2 Componentized Actions......................... 26
4.2.3 Action Embedding and Training Procedure....... 29
4.3 Numerical Results................................ 30
4.3.1 Simulation Setup.............................. 30
4.3.2 Performance Evaluation........................ 32
5 Conclusion.......................................... 38
Bibliography.......................................... 39
v
參考文獻 [1] N. T. Nguyen and K. Lee. Unequally Sub-Connected Architecture for Hybrid Beamforming in Massive MIMO Systems. IEEE Transactions on Wireless Communications, 19(2):1127–1140, 2020.
[2] H. Masoumi and M. J. Emadi. Performance Analysis of Cell-Free Massive MIMO System With Limited Fronthaul Capacity and Hardware Impairments. IEEE Transactions on Wireless Communications, 19(2):1038–1053, 2020.
[3] P. Liu, K. Luo, D. Chen, and T. Jiang. Spectral Efficiency Analysis of Cell-Free Massive MIMO Systems With Zero-Forcing Detector. IEEE Transactions on Wireless Communications, 19(2):795–807, 2020.
[4] E. Sadeghabadi, S. M. Azimi-Abarghouyi, B. Makki, M. Nasiri-Kenari, and T. Svensson. Asynchronous Downlink Massive MIMO Networks: A Stochastic Geometry Approach. IEEE Transactions on Wireless Communications, 19(1):579–594, 2020.
[5] C. D’Andrea, S. Buzzi, and M. Lops. Communications and Radar Coexistence in the Massive MIMO Regime: Uplink Analysis. IEEE Transactions on Wireless Communications, 19(1):19–33, 2020.
[6] J. Flordelis, X. Li, O. Edfors, and F. Tufvesson. Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation. IEEE Transactions on Wireless Communications, 19(1):380–394, 2020.
[7] G. Alfano, C. . Chiasserini, and A. Nordio. SINR and Multiuser Efficiency Gap Between MIMO Linear Receivers. IEEE Transactions onWireless Communications, 19(1):106–119, 2020.
[8] E. Bj¨ornson and L. Sanguinetti. Making Cell-Free Massive MIMOCompetitiveWith MMSE Processing and Centralized Implementation. IEEE Transactions on Wireless Communications, 19(1):77–90, 2020.
[9] S. Gao, X. Cheng, and L. Yang. Spatial Multiplexing With Limited RF Chains: Generalized Beamspace Modulation (GBM) for mmWave Massive MIMO. IEEE Journal on Selected Areas in Communications, 37(9):2029–2039, 2019.
[10] J. Liu and E. S. Bentley. Hybrid-Beamforming-Based Millimeter-Wave Cellular Network Optimization. IEEE Journal on Selected Areas in Communications, 37(12):2799–2813, 2019.
[11] D. Su, Y. Jiang, X. Wang, and X. Gao. Omnidirectional Precoding for Massive MIMO With Uniform Rectangular Array—Part I: Complementary Codes-Based Schemes. IEEE Transactions on Signal Processing, 67(18):4761–4771, 2019.
[12] S. Jacobsson, G. Durisi, M. Coldrey, and C. Studer. Linear Precoding With Low- Resolution DACs for Massive MU-MIMO-OFDM Downlink. IEEE Transactions on Wireless Communications, 18(3):1595–1609, 2019.
[13] S. S. Ioushua and Y. C. Eldar. A Family of Hybrid Analog–Digital Beamforming Methods for Massive MIMO Systems. IEEE Transactions on Signal Processing, 67(12):3243–3257, 2019.
[14] L. Chu, F.Wen, L. Li, and R. Qiu. Efficient Nonlinear Precoding for Massive MIMO Downlink Systems With 1-Bit DACs. IEEE Transactions on Wireless Communications, 18(9):4213–4224, 2019.
[15] M. Feng and S. Mao. Harvest The Potential of Massive MIMO with Multi-Layer Techniques. IEEE Network, 30(5):40–45, 2016.
[16] Tasher Ali Sheikh, Joyatri Bora, and Md Anwar Hussain. Capacity Maximizing in Massive MIMO with Linear Precoding for SSF and LSF Channel with Perfect CSI. Digital Communications and Networks, 2019.
[17] S. Lagen, A. Agustin, and J. Vidal. Joint User Scheduling, Precoder Design, and Transmit Direction Selection in MIMO TDD Small Cell Networks. IEEE Transactions on Wireless Communications, 16(4):2434–2449, 2017.
[18] J. Choi, N. Lee, S. Hong, and G. Caire. Joint User Scheduling, Power Allocation, and Precoding Design for Massive MIMO Systems: A Principal Component Analysis Approach. In 2018 IEEE International Symposium on Information Theory (ISIT),
pages 396–400, 2018.
[19] Y. Wei, F. R. Yu, M. Song, and Z. Han. User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach. IEEE Transactions on Wireless Communications, 17(1):680–692, 2018.
[20] R. Li, Z. Zhao, Q. Sun, C. I, C. Yang, X. Chen, M. Zhao, and H. Zhang. Deep Reinforcement Learning for Resource Management in Network Slicing. IEEE Access, 6:74429–74441, 2018.
[21] H. Ye, G. Y. Li, and B. F. Juang. Deep Reinforcement Learning Based Resource Allocation for V2V Communications. IEEE Transactions on Vehicular Technology, 68(4):3163–3173, 2019.
[22] S. Tseng, Z. Liu, Y. Chou, and C. Huang. Radio Resource Scheduling for 5G NR via Deep Deterministic Policy Gradient. In 2019 IEEE International Conference on Communications Workshops (ICC Workshops), pages 1–6, 2019.
[23] Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous Control with Deep Reinforcement Learning. In International Conference on Learning Representations
(ICLR), February 2016.
[24] Y. Shi, W.-H. Kuo, C.-W. Huang, Y.-C. Chou, S.-H. Fang, and D.-N. Yang. Crosslayer allocation scheme for multi-view videos in massive mimo networks. IEEE International Conference on Communications (IEEE ICC), June. 2020.
[25] ETSI. System Architecture for the 5G System. ETSI TS 123 501, 2019.
[26] Luat Do, Svitlana Zinger, and Peter HN deWith. Objective quality analysis for freeviewpoint dibr. In 2010 IEEE International Conference on Image Processing, pages 2629–2632. IEEE, 2010.
[27] Kashyap Kammachi Sreedhar, Alireza Aminlou, Miska M Hannuksela, and Moncef Gabbouj. Standard-compliant multiview video coding and streaming for virtual reality applications. In 2016 IEEE International Symposium on Multimedia (ISM).
IEEE, 2016.
[28] Leida Li, Yu Zhou, Ke Gu, Weisi Lin, and Shiqi Wang. Quality assessment of dibrsynthesized images by measuring local geometric distortions and global sharpness. IEEE Transactions on Multimedia, 20(4):914–926, 2018.
[29] Wen-Nung Lie, Chia-Yung Hsieh, and Guo-Shiang Lin. Key-frame-based background sprite generation for hole filling in depth image-based rendering. IEEE Transactions on Multimedia, 20(5):1075–1087, 2017.
[30] Shuai Li, Ce Zhu, and Ming-Ting Sun. Hole filling with multiple reference views in dibr view synthesis. IEEE Transactions on Multimedia, 20(8):1948–1959, 2018.
[31] Chi-Heng Lin, De-Nian Yang, Ji-Tang Lee, and Wanjiun Liao. Efficient errorresilient multicasting for multi-view 3d videos in wireless network. In 2016 IEEE Global Communications Conference (GLOBECOM). IEEE, 2016.
[32] Ji-Tang Lee, De-Nian Yang, Yu-Chun Chen, andWanjiun Liao. Efficient multi-view 3d video multicast with depth-image-based rendering in lte-advanced networks with carrier aggregation. IEEE Transactions on Mobile Computing, 17(1):85–98, 2018.
[33] X. Cheng, K. Xu, J. Sun, and S. Li. Adaptive Grouping Sparse Bayesian Learning for Channel Estimation in Non-Stationary Uplink Massive MIMO Systems. IEEE Transactions on Wireless Communications, 18(8):4184–4198, 2019.
[34] Z. Xie and W. Chen. A Joint Channel and Queue Aware Scheduling Method for Multi-User Massive MIMO Systems. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pages 1–6, 2019.
[35] X. Chen, A. Liu, Y. Cai, V. K. N. Lau, and M. Zhao. Randomized Two-Timescale Hybrid Precoding for Downlink Multicell Massive MIMO Systems. IEEE Transactions on Signal Processing, 67(16):4152–4167, 2019.
[36] M. Wen, B. Zheng, K. J. Kim, M. Di Renzo, T. A. Tsiftsis, K. Chen, and N. Al- Dhahir. A Survey on Spatial Modulation in Emerging Wireless Systems: Research Progresses and Applications. IEEE Journal on Selected Areas in Communications,
37(9):1949–1972, 2019.
[37] A. Bereyhi, M. A. Sedaghat, R. R. M¨uller, and G. Fischer. GLSE Precoders for Massive MIMO Systems: Analysis and Applications. IEEE Transactions on Wireless Communications, 18(9):4450–4465, 2019.
[38] G. Kwon and H. Park. A Joint Scheduling and Millimeter Wave Hybrid Beamforming System with Partial Side Information. In 2016 IEEE International Conference on Communications (ICC), pages 1–6, 2016.
[39] Y. Xu, G. Yue, N. Prasad, S. Rangarajan, and S. Mao. User Grouping and Scheduling for Large Scale MIMO Systems with Two-Stage Precoding. In 2014 IEEE International Conference on Communications (ICC), pages 5197–5202, 2014.
[40] A. Almradi, M. Matthaiou, P. Xiao, and V. F. Fusco. Hybrid Precoding for Massive MIMO With Low Rank Channels: A Two-Stage User Scheduling Approach. IEEE Transactions on Communications, 68(8):4816–4831, 2020.
[41] M. Al-Saedy, M. Al-Imari, M. Al-Shuraifi, and H. Al-Raweshidy. Joint User Selection and Multimode Scheduling in Multicell MIMO Cellular Networks. IEEE Transactions on Vehicular Technology, 66(12):10962–10972, 2017.
[42] T. Xie, L. Dai, D. W. K. Ng, and C. Chae. On the Power Leakage Problem in Millimeter-Wave Massive MIMO With Lens Antenna Arrays. IEEE Transactions on Signal Processing, 67(18):4730–4744, 2019.
[43] M. Olyaee, M. Eslami, and J. Haghighat. An Energy-Efficient Joint Antenna and User Selection Algorithm for Multi-User Massive MIMO Downlink. IET Communications, 12(3):255–260, 2018.
[44] S. Singh, M. Geraseminko, S. Yeh, N. Himayat, and S. Talwar. Proportional Fair Traffic Splitting and Aggregation in HeterogeneousWireless Networks. IEEE Communications Letters, 20(5):1010–1013, 2016.
[45] C. Jiang, H. Zhang, Y. Ren, Z. Han, K. Chen, and L. Hanzo. Machine Learning Paradigms for Next-Generation Wireless Networks. IEEE Wireless Communications, 24(2):98–105, 2017.
[46] C. Fiandrino, C. Zhang, P. Patras, A. Banchs, and J. Widmer. A Machine-Learning- Based Framework for Optimizing the Operation of Future Networks. IEEE Communications Magazine, 58(6):20–25, 2020.
[47] Y. Yang, Y. Li, K. Li, S. Zhao, R. Chen, J.Wang, and S. Ci. DECCO: Deep-Learning Enabled Coverage and Capacity Optimization for Massive MIMO Systems. IEEE Access, 6:23361–23371, 2018.
[48] A. Kilzi, J. Farah, C. Abdel Nour, and C. Douillard. Mutual Successive Interference Cancellation Strategies in NOMA for Enhancing the Spectral Efficiency of CoMP Systems. IEEE Transactions on Communications, 68(2):1213–1226, 2020.
[49] Min Huang, Fang Yuan, Hong Cheng, and Xu Zhang. Design of Low-Latency Uplink MAC Scheduling for Massive MIMO-OFDM Systems. In 2017 IEEE International Conference on Communications Workshops (ICC Workshops), pages 632–
638, 2017.
[50] H. Zhang, N. Yang, W. Huangfu, K. Long, and V. C. M. Leung. Power Control Based on Deep Reinforcement Learning for Spectrum Sharing. IEEE Transactions on Wireless Communications, 19(6):4209–4219, 2020.
[51] C. Jiang and X. Zhu. Reinforcement Learning Based Capacity Management in Multi-Layer Satellite Networks. IEEE Transactions on Wireless Communications, 19(7):4685–4699, 2020.
[52] Y. Ouyang, M. Gagrani, and R. Jain. Posterior Sampling-Based Reinforcement Learning for Control of Unknown Linear Systems. IEEE Transactions on Automatic Control, 65(8):3600–3607, 2020.
[53] Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous Control with Deep Reinforcement Learning. arXiv preprint arXiv:1509.02971, 2015.
[54] W. Kuo and Y. Lu. Antenna Allocation Scheme for Providing Scalable Video Coded Streams Over Multiple-Base-Station Massive MIMO Cellular Networks. IEEE Transactions on Vehicular Technology, 67(3):2314–2323, 2018.
[55] Christoph Fehn. Depth-image-based rendering (dibr), compression, and transmission for a new approach on 3d-tv. In Stereoscopic Displays and Virtual Reality Systems XI, volume 5291, pages 93–104. International Society for Optics and Photonics, 2004.
[56] Feng Shao, Gangyi Jiang, Mei Yu, Ken Chen, and Yo-Sung Ho. Asymmetric coding of multi-view video plus depth based 3-d video for view rendering. IEEE Transactions on Multimedia, 1(14):157–167, 2012.
[57] Shane Alcock and Richard Nelson. Application flow control in youtube video streams. ACM SIGCOMM Computer Communication Review, 41(2):24–30, 2011.
[58] I Mpeg. Information technology-dynamic adaptive streaming over http (dash)-part 1: Media presentation description and segment formats. ISO/IEC MPEG, Tech. Rep, 2014.
[59] Roberto Viola, Angel Martin, Mikel Zorrilla, and Jon Montalb´an. Mec proxy for efficient cache and reliable multi-cdn video distribution. In 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pages 1–7. IEEE, 2018.
[60] Ahmed Hamza and Mohamed Hefeeda. Energy-efficient multicasting of multiview 3d videos to mobile devices. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 8(3s):1–25, 2012.
[61] Yagiz Aksoy, Ozan Sener, Aydin Alatan, and Kemal Ugur. Interactive 2d-3d image conversion for mobile devices. In 2012 19th IEEE International Conference on Image Processing, pages 2729–2732. IEEE, 2012.
[62] Matthew Borg and Carl James Debono. Fast high definition video rendering on mobile devices. In 2016 18th Mediterranean Electrotechnical Conference (MELECON), pages 1–6. IEEE, 2016.
[63] Wei Xu, Yuzhuo Wei, Ying Cui, and Zhi Liu. Energy-efficient multi-view video transmission with view synthesis-enabled multicast. In 2018 IEEE Global Communications Conference (GLOBECOM), pages 1–7. IEEE, 2018.
[64] Cha Zhang and Zhengyou Zhang. Calibration between depth and color sensors for commodity depth cameras. In Computer vision and machine learning with RGB-D sensors, pages 47–64. Springer, 2014.
[65] A. Alorainy and M. J. Hossain. Cross-layer performance of channel scheduling mechanisms in small-cell networks with non-line-of-sight wireless backhaul links. IEEE Transactions on Wireless Communications, 14(9):4907–4922, Sep. 2015.
[66] K. Xiao, S. Mao, and J. K. Tugnait. QoE-Driven Resource Allocation for DASH over OFDMA Networks. In 2016 IEEE Global Communications Conference (GLOBECOM), pages 1–6, Dec 2016.
[67] Hamed Ahmadi, Omar Eltobgy, and Mohamed Hefeeda. Adaptive multicast streaming of virtual reality content to mobile users. In Proceedings of the on Thematic Workshops of ACM Multimedia 2017, pages 170–178, New York, NY, USA, 2017.
ACM.
[68] Mojang. Minecraft. https://www.minecraft.net, 2009.
[69] Craftbukkit. https://getbukkit.org, 2009.
[70] Xikage. Mythicmobs. https://www.spigotmc.org/resources/mythicmobs-freeversion-the-1-custom-mob-creator.5702, 2015.
[71] Gurobi optimization. https://www.gurobi.com/, 2008.
[72] S. Adeshina Busari, K. Mohammed Saidul Huq, G. Felfel, and J. Rodriguez. Adaptive Resource Allocation for Energy-Efficient Millimeter-Wave Massive MIMO Networks. In 2018 IEEE Global Communications Conference (GLOBECOM), pages 1–6, 2018.
[73] Z. Chen, E. Bjornson, and E. G. Larsson. Dynamic Scheduling and Power Control in Uplink Massive MIMO with Random Data Arrivals. In ICC 2019 - 2019 IEEE International Conference on Communications (ICC), pages 1–6, 2019.
[74] J. Guo, Q. Yu, W. Meng, and W. Xiang. Energy-Efficient Hybrid Precoder With Adaptive Overlapped Subarrays for Large-Array mmWave Systems. IEEE Transactions on Wireless Communications, 19(3):1484–1502, 2020.
[75] L. Jiang and H. Jafarkhani. mmWave Amplify-and-Forward MIMO Relay Networks With Hybrid Precoding/Combining Design. IEEE Transactions onWireless Communications, 19(2):1333–1346, 2020.
[76] J. Ye and Y. A. Zhang. DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks. IEEE Transactions on Mobile Computing, 19(9):2076–2087, 2020.
[77] Na Guan, Y. Zhou, Lin Tian, Gang Sun, and Jinglin Shi. QoS Guaranteed Resource Block Allocation Algorithm for LTE Systems. In 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), pages 307–312, 2011.
[78] Hoon Kim and Youngnam Han. A Proportional Fair Scheduling for Multicarrier Transmission Systems. IEEE Communications Letters, 9(3):210–212, 2005.
[79] R. M´endez-Rial, C. Rusu, A. Alkhateeb, N. Gonz´alez-Prelcic, and R. W. Heath. Channel Estimation and Hybrid Combining for mmWave: Phase Shifters or Switches? In 2015 Information Theory and Applications Workshop (ITA), pages 90–97, 2015.
[80] Reuven Y Rubinstein and Dirk P Kroese. The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer Science & Business Media, 2013.
[81] X. Gao, L. Dai, Y. Sun, S. Han, and I. Chih-Lin. Machine Learning Inspired Energy-Efficient Hybrid Precoding for mmWave Massive MIMO Systems. In 2017 IEEE International Conference on Communications (ICC), pages 1–6, 2017.
[82] Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, and Ben Coppin. Deep Reinforcement Learning in Large Discrete Action Spaces. arXiv preprint arXiv:1512.07679, 2015.
[83] T. Girici, C. Zhu, J. R. Agre, and A. Ephremides. Proportional Fair Scheduling Algorithm in OFDMA-Based Wireless Systems With QoS Constraints. Journal of Communications and Networks, 12(1):30–42, 2010.
指導教授 黃志煒 審核日期 2020-8-20
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   

若有論文相關問題，請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407，或E-mail聯絡  - 隱私權政策聲明