Paper-with-Code-of-Wireless-communication-Based-on-DL

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[{"_id":"6343db6209a35269b77fcfb0","body":"L. Huang, S. Bi, and Y. J. Zhang, \u201cDeep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks,\u201d IEEE Trans. Mobile Compt., vol. 19, no. 11, pp. 2581-2593, November 2020.\r\n\r\nhttps:\/\/github.com\/revenol\/DROO","issue_id":1660402460823,"origin_id":782617918,"user_origin_id":19899894,"create_time":1613823105,"update_time":1613823105,"id":1665391458538,"updated_at":"2022-10-10T08:44:18.538000Z","created_at":"2022-10-10T08:44:18.538000Z"},{"_id":"6343db6209a35269b77fcfb1","body":"J. Wang, J.Hu, G. Min, A. Y. Zomaya, and N. Georgalas, \u201cFast Adaptive Computation Offloading in Edge Computing based on Meta Reinforcement Learning\u201c. IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 1, pp. 242--253, 2021.\r\n\r\nhttps:\/\/arxiv.org\/abs\/2008.02033\r\n\r\nhttps:\/\/github.com\/linkpark\/metarl-offloading \r\n\r\n","issue_id":1660402460823,"origin_id":800568385,"user_origin_id":5644942,"create_time":1615925351,"update_time":1615925351,"id":1665391458543,"updated_at":"2022-10-10T08:44:18.543000Z","created_at":"2022-10-10T08:44:18.543000Z"},{"_id":"6343db6209a35269b77fcfb2","body":"\u5efa\u8bae\u5728\u8868\u683c\u7684\u6bcf\u4e00\u884c\u524d\u6dfb\u52a0\u5e8f\u53f7 \u65b9\u4fbf\u5927\u5bb6\u77e5\u9053\u6700\u8fd1\u6dfb\u52a0\u7684\u662f\u54ea\u4e9b\u8bba\u6587\u4ee3\u7801","issue_id":1660402460823,"origin_id":815532325,"user_origin_id":63907928,"create_time":1617867764,"update_time":1617867764,"id":1665391458547,"updated_at":"2022-10-10T08:44:18.547000Z","created_at":"2022-10-10T08:44:18.547000Z"},{"_id":"6343db6209a35269b77fcfb3","body":"> \u5efa\u8bae\u5728\u8868\u683c\u7684\u6bcf\u4e00\u884c\u524d\u6dfb\u52a0\u5e8f\u53f7 \u65b9\u4fbf\u5927\u5bb6\u77e5\u9053\u6700\u8fd1\u6dfb\u52a0\u7684\u662f\u54ea\u4e9b\u8bba\u6587\u4ee3\u7801\r\n\r\n\u4e00\u822c\u6700\u524d\u9762\u7684\u5c31\u662f\u65b0\u6dfb\u52a0\u7684\uff0c\u4f46\u662f\u4e0d\u80fd\u4fdd\u8bc1\u65b0\u6dfb\u52a0\u8bba\u6587\u53d1\u8868\u7684\u65f6\u95f4\u4e5f\u662f\u6700\u65b0\u7684\u3002","issue_id":1660402460823,"origin_id":817250925,"user_origin_id":12268675,"create_time":1618118603,"update_time":1618118603,"id":1665391458551,"updated_at":"2022-10-10T08:44:18.550000Z","created_at":"2022-10-10T08:44:18.550000Z"},{"_id":"6343db6209a35269b77fcfb5","body":"K. Pratik, B. D. Rao, and M. Welling, \u201cRE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection,\u201d IEEE Trans. Signal Process., vol. 69, pp. 459\u2013473, 2021.\r\n\r\nhttps:\/\/arxiv.org\/abs\/2007.00140\r\n\r\nhttps:\/\/github.com\/krpratik\/RE-MIMO","issue_id":1660402460823,"origin_id":822878384,"user_origin_id":37243940,"create_time":1618878156,"update_time":1618878156,"id":1665391458555,"updated_at":"2022-10-10T08:44:18.555000Z","created_at":"2022-10-10T08:44:18.555000Z"},{"_id":"6343db6209a35269b77fcfb7","body":"F. B. Mismar, A. Alammouri, A. Alkhateeb, J. G. Andrews, and B. L. Evans, \u201cDeep Learning Predictive Band Switching in Wireless Networks,\u201d IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 96\u2013109, Jan. 2021, doi: 10.1109\/TWC.2020.3023397.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/abstract\/document\/9199558\r\n\r\nhttps:\/\/github.com\/farismismar\/Bandswitch-DeepMIMO","issue_id":1660402460823,"origin_id":828900219,"user_origin_id":34416569,"create_time":1619663227,"update_time":1619663227,"id":1665391458559,"updated_at":"2022-10-10T08:44:18.559000Z","created_at":"2022-10-10T08:44:18.559000Z"},{"_id":"6343db6209a35269b77fcfb9","body":"H. Chang, H. Song, Y. Yi, J. Zhang, H. He and L. Liu, \"Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach,\" in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1938-1948, April 2019, doi: 10.1109\/JIOT.2018.2872441.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/document\/8474348\r\nhttps:\/\/github.com\/haohsuan2918\/DQN_RC_DSA_IOT2019","issue_id":1660402460823,"origin_id":840605596,"user_origin_id":51740966,"create_time":1620916671,"update_time":1620916671,"id":1665391458563,"updated_at":"2022-10-10T08:44:18.563000Z","created_at":"2022-10-10T08:44:18.563000Z"},{"_id":"6343db6209a35269b77fcfba","body":"https:\/\/mlc.committees.comsoc.org\/papers-with-code\/","issue_id":1660402460823,"origin_id":840607727,"user_origin_id":51740966,"create_time":1620916873,"update_time":1620916873,"id":1665391458566,"updated_at":"2022-10-10T08:44:18.565000Z","created_at":"2022-10-10T08:44:18.565000Z"},{"_id":"6343db6209a35269b77fcfbb","body":"Suzhi Bi, Liang Huang, Hui Wang, and Ying-Jun Angela Zhang, \"Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks,\" IEEE Transactions on Wireless Communications, 2021, doi:10.1109\/TWC.2021.3085319.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/document\/9449944\r\nhttps:\/\/github.com\/revenol\/LyDROO","issue_id":1660402460823,"origin_id":866040757,"user_origin_id":19899894,"create_time":1624372659,"update_time":1624372659,"id":1665391458569,"updated_at":"2022-10-10T08:44:18.568000Z","created_at":"2022-10-10T08:44:18.568000Z"},{"_id":"6343db6209a35269b77fcfbc","body":"H. He, C. Wen, S. Jin, and G. Y. Li, \u201cModel-driven deep learning for MIMO detection,\u201d IEEE Trans. Signal Process., vol. 68, pp. 1702\u20131715, Feb. 2020.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/document\/9018199\/\r\nhttps:\/\/github.com\/hehengtao\/OAMP-Net","issue_id":1660402460823,"origin_id":884743687,"user_origin_id":37243940,"create_time":1626942805,"update_time":1626942805,"id":1665391458571,"updated_at":"2022-10-10T08:44:18.571000Z","created_at":"2022-10-10T08:44:18.571000Z"},{"_id":"6343db6209a35269b77fcfbd","body":"J. Choi, Y. Cho, B. L. Evans and A. Gatherer, \"Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals,\" 2019 IEEE Global Communications Conference (GLOBECOM), 2019, pp. 1-6, doi: 10.1109\/GLOBECOM38437.2019.9013332.\r\n\r\nhttps:\/\/github.com\/Yunseong-Cho\/LearningML\r\n","issue_id":1660402460823,"origin_id":911292449,"user_origin_id":32814213,"create_time":1630566453,"update_time":1630566453,"id":1665391458574,"updated_at":"2022-10-10T08:44:18.574000Z","created_at":"2022-10-10T08:44:18.574000Z"},{"_id":"6343db6209a35269b77fcfbf","body":"Lee M, Yu G, Li G Y. Graph Embedding-Based Wireless Link Scheduling With Few Training Samples[J]. IEEE Transactions on Wireless Communications, 2020, 20(4): 2282-2294.\r\n\r\nhttps:\/\/github.com\/mengyuan-lee\/graph_embedding_link_scheduling","issue_id":1660402460823,"origin_id":961567050,"user_origin_id":45653880,"create_time":1636077346,"update_time":1636077346,"id":1665391458578,"updated_at":"2022-10-10T08:44:18.578000Z","created_at":"2022-10-10T08:44:18.578000Z"},{"_id":"6343db6209a35269b77fcfc0","body":"Rui Li, Ondrej Bohdal, Rajesh K. Mishra, Hyeji Kim, Da Li, Nicholas Donald Lane, and Timothy Hospedales. \"A Channel Coding Benchmark for Meta-Learning.\" NeurIPS 2021 Datasets and Benchmarks Track\r\n\r\nhttps:\/\/openreview.net\/forum?id=DjzPaX8AT0z \r\nhttps:\/\/github.com\/ruihuili\/MetaCC","issue_id":1660402460823,"origin_id":962164964,"user_origin_id":19306378,"create_time":1636140775,"update_time":1636235559,"id":1665391458585,"updated_at":"2022-10-10T08:44:18.585000Z","created_at":"2022-10-10T08:44:18.585000Z"},{"_id":"6343db6209a35269b77fcfc1","body":"J. Wang, J.Hu, G. Min, W. Zhan, A. Y. Zomaya, and N. Georgalas, \"Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning.\" IEEE Transactions on Computers, 2021.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/abstract\/document\/9627763 \r\nhttps:\/\/github.com\/linkpark\/RLTaskOffloading \r\n\r\n","issue_id":1660402460823,"origin_id":986154397,"user_origin_id":5644942,"create_time":1638671289,"update_time":1638671289,"id":1665391458588,"updated_at":"2022-10-10T08:44:18.588000Z","created_at":"2022-10-10T08:44:18.588000Z"},{"_id":"6343db6209a35269b77fcfc2","body":"Q. Hu, Y. Liu, Y. Cai, G. Yu, and Z. Ding, \u201cJoint deep reinforcement learning and unfolding: Beam selection and precoding for\r\nmmWave multiuser MIMO with lens arrays,\u201d IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2289\u20132304, Jun. 2021.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/stamp\/stamp.jsp?tp=&arnumber=9448095\r\nhttps:\/\/github.com\/hqyyqh888\/DDQN_BeamSelection","issue_id":1660402460823,"origin_id":993746010,"user_origin_id":27207105,"create_time":1639500272,"update_time":1639500272,"id":1665391458591,"updated_at":"2022-10-10T08:44:18.591000Z","created_at":"2022-10-10T08:44:18.591000Z"},{"_id":"6343db6209a35269b77fcfc3","body":"\u60a8\u597d\uff0c\u60a8\u7684\u6765\u4fe1\u6211\u5df2\u6536\u5230\u3002","issue_id":1660402460823,"origin_id":993746780,"user_origin_id":5644942,"create_time":1639500291,"update_time":1639500291,"id":1665391458594,"updated_at":"2022-10-10T08:44:18.594000Z","created_at":"2022-10-10T08:44:18.594000Z"},{"_id":"6343db6209a35269b77fcfc5","body":"S. Wang, S. Bi and Y. -J. A. Zhang, \"Deep Reinforcement Learning with Communication Transformer for Adaptive Live Streaming in Wireless Edge Networks,\" in IEEE Journal on Selected Areas in Communications, doi: 10.1109\/JSAC.2021.3126062.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/document\/9605672\r\nhttps:\/\/github.com\/wsyCUHK\/SACCT","issue_id":1660402460823,"origin_id":994270624,"user_origin_id":37823466,"create_time":1639540955,"update_time":1639540955,"id":1665391458598,"updated_at":"2022-10-10T08:44:18.598000Z","created_at":"2022-10-10T08:44:18.598000Z"},{"_id":"6343db6209a35269b77fcfc6","body":"H. Lu, M. Jiang and J. Cheng, \"Deep Learning Aided Robust Joint Channel Classification, Channel Estimation, and Signal Detection for Underwater Optical Communication,\" in IEEE Transactions on Communications, vol. 69, no. 4, pp. 2290-2303, April 2021, doi: 10.1109\/TCOMM.2020.3046659.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/document\/9302692\r\nhttps:\/\/github.com\/Huaiyin-Lu\/UWOC-JCCESD","issue_id":1660402460823,"origin_id":1073529239,"user_origin_id":32814213,"create_time":1647845125,"update_time":1647845125,"id":1665391458601,"updated_at":"2022-10-10T08:44:18.601000Z","created_at":"2022-10-10T08:44:18.601000Z"},{"_id":"6343db6209a35269b77fcfc8","body":"\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\uff0c\u795d\u597d~\uff01","issue_id":1660402460823,"origin_id":1073529357,"user_origin_id":5592886,"create_time":1647845145,"update_time":1647845145,"id":1665391458605,"updated_at":"2022-10-10T08:44:18.604000Z","created_at":"2022-10-10T08:44:18.604000Z"},{"_id":"6343db6209a35269b77fcfca","body":"\u4f60\u597d\uff0c\u4f60\u7684\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\u3002","issue_id":1660402460823,"origin_id":1073529408,"user_origin_id":33373541,"create_time":1647845155,"update_time":1647845155,"id":1665391458608,"updated_at":"2022-10-10T08:44:18.608000Z","created_at":"2022-10-10T08:44:18.608000Z"},{"_id":"6343db6209a35269b77fcfcb","body":"Z. He, L. Wang, H. Ye, G. Y. Li and B. -H. F. Juang, \"Resource Allocation based on Graph Neural Networks in Vehicular Communications,\" GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 1-5, doi: 10.1109\/GLOBECOM42002.2020.9322537.\r\n\r\nhttps:\/\/github.com\/Coolzyh\/Globecom2020-ResourceAllocationGNN","issue_id":1660402460823,"origin_id":1088511689,"user_origin_id":15089063,"create_time":1649153111,"update_time":1649153111,"id":1665391458614,"updated_at":"2022-10-10T08:44:18.613000Z","created_at":"2022-10-10T08:44:18.613000Z"},{"_id":"6343db6209a35269b77fcfcc","body":"\u4f60\u597d\uff0c\u4f60\u7684\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\u3002","issue_id":1660402460823,"origin_id":1088511986,"user_origin_id":33373541,"create_time":1649153130,"update_time":1649153130,"id":1665391458616,"updated_at":"2022-10-10T08:44:18.616000Z","created_at":"2022-10-10T08:44:18.616000Z"},{"_id":"6343db6209a35269b77fcfcd","body":"\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\uff0c\u795d\u597d~\uff01","issue_id":1660402460823,"origin_id":1088512003,"user_origin_id":5592886,"create_time":1649153131,"update_time":1649153131,"id":1665391458619,"updated_at":"2022-10-10T08:44:18.618000Z","created_at":"2022-10-10T08:44:18.618000Z"},{"_id":"6343db6209a35269b77fcfcf","body":"\u7fa4\u4eba\u6570\u8d85\u8fc7200\uff0c\u8fdb\u4e0d\u53bb\u4e86\u3002","issue_id":1660402460823,"origin_id":1120380708,"user_origin_id":89723771,"create_time":1652001188,"update_time":1652001188,"id":1665391458622,"updated_at":"2022-10-10T08:44:18.622000Z","created_at":"2022-10-10T08:44:18.622000Z"},{"_id":"6343db6209a35269b77fcfd0","body":"\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\uff0c\u795d\u597d~\uff01","issue_id":1660402460823,"origin_id":1120380803,"user_origin_id":5592886,"create_time":1652001210,"update_time":1652001210,"id":1665391458625,"updated_at":"2022-10-10T08:44:18.625000Z","created_at":"2022-10-10T08:44:18.625000Z"},{"_id":"6343db6209a35269b77fcfd1","body":"\u4f60\u597d\uff0c\u4f60\u7684\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\u3002","issue_id":1660402460823,"origin_id":1120380805,"user_origin_id":33373541,"create_time":1652001211,"update_time":1652001211,"id":1665391458628,"updated_at":"2022-10-10T08:44:18.628000Z","created_at":"2022-10-10T08:44:18.628000Z"},{"_id":"6343db6209a35269b77fcfd2","body":"Wang, J., Hu, J., Min, G., Ni, Q., & El-Ghazawi, T. (2022). Online Service Migration in Mobile Edge with Incomplete System Information: A Deep Recurrent Actor-Critic Learning Approach.\u00a0IEEE Transactions on Mobile Computing.\r\n\r\nhttps:\/\/ieeexplore.ieee.org\/document\/9853218\r\nhttps:\/\/github.com\/linkpark\/pomdp-service-migration","issue_id":1660402460823,"origin_id":1218097478,"user_origin_id":5644942,"create_time":1660746944,"update_time":1660746944,"id":1665391458631,"updated_at":"2022-10-10T08:44:18.631000Z","created_at":"2022-10-10T08:44:18.631000Z"},{"_id":"6343db6209a35269b77fcfd3","body":"\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\uff0c\u795d\u597d~\uff01","issue_id":1660402460823,"origin_id":1218097852,"user_origin_id":5592886,"create_time":1660746962,"update_time":1660746962,"id":1665391458634,"updated_at":"2022-10-10T08:44:18.633000Z","created_at":"2022-10-10T08:44:18.633000Z"},{"_id":"6343db6209a35269b77fcfd4","body":"\u4f60\u597d\uff0c\u4f60\u7684\u90ae\u4ef6\u5df2\u7ecf\u6536\u5230\uff0c\u8c22\u8c22\u3002","issue_id":1660402460823,"origin_id":1218097885,"user_origin_id":33373541,"create_time":1660746964,"update_time":1660746964,"id":1665391458636,"updated_at":"2022-10-10T08:44:18.636000Z","created_at":"2022-10-10T08:44:18.636000Z"},{"_id":"6343db6209a35269b77fcfd5","body":"L. Zhang, J. Tan, Y. -C. Liang, G. Feng and D. Niyato, \"Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks,\" in IEEE Transactions on Wireless Communications, vol. 18, no. 6, pp. 3281-3294, June 2019, doi: 10.1109\/TWC.2019.2912754.\r\n\r\nurl:https:\/\/ieeexplore.ieee.org\/document\/8703432","issue_id":1660402460823,"origin_id":1237584874,"user_origin_id":112915501,"create_time":1662430445,"update_time":1662430445,"id":1665391458639,"updated_at":"2022-10-10T08:44:18.639000Z","created_at":"2022-10-10T08:44:18.639000Z"}]

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Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

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L. Zhang, J. Tan, Y. -C. Liang, G. Feng and D. Niyato, "Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks," in IEEE Transactions on Wireless Communications,...

Msixram

new deep unfolding paper in physical layer

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Paper title: Learn_to_Optimize_RIS_Aided_Hybrid_Beamforming_With_Out-of-Distribution_Generalization Code webpage: https://github.com/hexingit/RIS-aided-HBF Paper webpage: https://ieeexplore.ieee.org/document/10477515

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信道预测数据集

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Joint Spectrum, Precoding, and Phase Shifts Design for RIS-Aided Multiuser MIMO THz Systems

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Hi Our new paper is recently accepted to IEEE TCOM, introducing MHGphormer for Terahertz systems. Here is the link for the paper and code. https://ieeexplore.ieee.org/abstract/document/10472941 https://github.com/Ali-Meh619/MHGphormer I appreciate it if...

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