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 1 | With the development of deep learning, more and more research has been done to solve problems in related communication fields using deep learning. As a graduate student in communications, if the lab does not have the code to accumulate in the relevant direction, it will be very difficult to get started and go deep into a new direction. At the same time, most papers in the field of communication do not provide open source code, and reproducible research is difficult.<br>The communication papers based on deep learning have increased rapidly in recent years, and the authors of these papers are more willing to open source. This project focuses on the collection of papers which are relevant to wireless communication based on deep learning and released the code  .<br>The area of personal attention and limited efforts, this list will not be so complete. If you know some related open source papers, but not on this list, you are welcome to pull request.
 2 | 
 3 | 
 4 | 
 5 | TODO
 6 | 
 7 | - [ ] Sort by different subdirections
 8 | - [ ] Add download link to paper
 9 | - [ ] Add more related paper's code
10 | - [ ] Traditional communication paper code list
11 | - [ ] "Communication +DL" paper list (high cited, without code is ok)
12 | 
13 | | Paper                                                        | Code                                                         |
14 | | ------------------------------------------------------------ | ------------------------------------------------------------ |
15 | | DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications | [The DeepMIMO Dataset](http://deepmimo.net/)                 |
16 | | Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels  | [yihanjiang](https://github.com/yihanjiang)/[turboae](https://github.com/yihanjiang/turboae) |
17 | | Communication Algorithms via Deep Learning  | [yihanjiang](https://github.com/yihanjiang)/[commviadl](https://github.com/yihanjiang/Sequential-RNN-Decoder) |
18 | | Fast Deep Learning for Automatic Modulation Classification   | [dl4amc](https://github.com/dl4amc)/[source](https://github.com/dl4amc/source) |
19 | | Deep Learning-Based Channel Estimation                       | [Mehran-Soltani](https://github.com/Mehran-Soltani)/[ChannelNet](https://github.com/Mehran-Soltani/ChannelNet) |
20 | | Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication | [seotaijiya](https://github.com/seotaijiya)/[TPC_D2D](https://github.com/seotaijiya/TPC_D2D) |
21 | | Deep learning-based channel estimation for beamspace mmWave massive MIMO systems | [hehengtao](https://github.com/hehengtao)/[LDAMP_based-Channel-estimation](https://github.com/hehengtao/LDAMP_based-Channel-estimation) |
22 | | Spatial deep learning for wireless scheduling                | [willtop](https://github.com/willtop)/[Spatial_DeepLearning_Wireless_Scheduling](https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling) |
23 | | Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach | [swordest](https://github.com/swordest)/[mec_drl](https://github.com/swordest/mec_drl) |
24 | | A deep-reinforcement learning approach for software-defined networking routing optimization | [knowledgedefinednetworking / a-deep-rl-approach-for-sdn-routing-optimization](https://github.com/knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization) |
25 | | Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells | [farismismar / Q-Learning-Power-Control](https://github.com/farismismar/Q-Learning-Power-Control) |
26 | | Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks | [bmatthiesen / deep-EE-opt](https://github.com/bmatthiesen/deep-EE-opt) |
27 | | Actor-Critic-Based Resource Allocation for Multi-modal Optical Networks | [BoyuanYan / Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks](https://github.com/BoyuanYan/Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks) |
28 | | Deep MIMO Detection                                          | [neevsamuel](https://github.com/neevsamuel)/[DeepMIMODetection](https://github.com/neevsamuel/DeepMIMODetection) |
29 | | Learning to Detect                                           | [neevsamuel](https://github.com/neevsamuel)/[LearningToDetect](https://github.com/neevsamuel/LearningToDetect) |
30 | | An iterative BP-CNN architecture for channel decoding        | [liangfei-info](https://github.com/liangfei-info)/[Iterative-BP-CNN](https://github.com/liangfei-info/Iterative-BP-CNN) |
31 | | On Deep Learning-Based Channel Decoding                      | [gruberto/DL-ChannelDecoding](https://github.com/gruberto/DL-ChannelDecoding) |
32 | | DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls | [ruihuili / DELMU](https://github.com/ruihuili/DELMU)        |
33 | | Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement | [farismismar / Deep-Q-Learning-SON-Perf-Improvement](https://github.com/farismismar/Deep-Q-Learning-SON-Perf-Improvement) |
34 | | An Introduction to Deep Learning for the Physical Layer      | [yashcao / RTN-DL-for-physical-layer](https://github.com/yashcao/RTN-DL-for-physical-layer)<br />[musicbeer / Deep-Learning-for-the-Physical-Layer](https://github.com/musicbeer/Deep-Learning-for-the-Physical-Layer)<br />[helloMRDJ / autoencoder-for-the-Physical-Layer](https://github.com/helloMRDJ/autoencoder-for-the-Physical-Layer) |
35 | | Convolutional Radio Modulation Recognition Networks          | [chrisruk](https://github.com/chrisruk)/[cnn](https://github.com/chrisruk/cnn)<br />[qieaaa / Singal-CNN](https://github.com/qieaaa/Singal-CNN) |
36 | | Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks | [zhongyuanzhao / dl_ofdm](https://github.com/zhongyuanzhao/dl_ofdm) |
37 | | Joint Transceiver Optimization for WirelessCommunication PHY with Convolutional NeuralNetwork | [hlz1992/RadioCNN](https://github.com/hlz1992/RadioCNN)      |
38 | | Deep Learning for Massive MIMO CSI Feedback                  | [sydney222 / Python_CsiNet](https://github.com/sydney222/Python_CsiNet) |
39 | | [Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning](http://arxiv.org/abs/1904.03657) | [TianLin0509/BF-design-with-DL](https://github.com/TianLin0509/BF-design-with-DL)|
40 | | 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning | [lasseufpa](https://github.com/lasseufpa)/[5gm-data](https://github.com/lasseufpa/5gm-data) |
41 | | Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks | [shkrwnd](https://github.com/shkrwnd)/[Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access](https://github.com/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access) |
42 | | DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning | [zaxliu](https://github.com/zaxliu)/[deepnap](https://github.com/zaxliu/deepnap) |
43 | | Automatic Modulation Classification: A Deep Learning Enabled Approach | [mengxiaomao](https://github.com/mengxiaomao)/[CNN_AMC](https://github.com/mengxiaomao/CNN_AMC) |
44 | | Deep Architectures for Modulation Recognition                | [qieaaa / Deep-Architectures-for-Modulation-Recognition](https://github.com/qieaaa/Deep-Architectures-for-Modulation-Recognition) |
45 | | Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks | [mkoz71 / Energy-Efficiency-in-Reinforcement-Learning](https://github.com/mkoz71/Energy-Efficiency-in-Reinforcement-Learning) |
46 | | Learning to optimize: Training deep neural networks for wireless resource management | [Haoran-S / DNN_WMMSE](https://github.com/Haoran-S/DNN_WMMSE) |
47 | | Implications of Decentralized Q-learning Resource Allocation in Wireless Networks | [wn-upf / decentralized_qlearning_resource_allocation_in_wns](https://github.com/wn-upf/decentralized_qlearning_resource_allocation_in_wns) |
48 | | Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems | [haoyye/OFDM_DNN](https://github.com/haoyye/OFDM_DNN)        |
49 | 
50 | 
51 | 
52 | Contributors：
53 | 
54 | [WxZhu](https://github.com/zhuwenxing)
55 | 
56 | 
57 | 
58 | Updates：
59 | 
60 | 1. complete first version ：2019-02-21
61 | 


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/README.md:
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  1 | For English reader,please refer to [English Version](https://github.com/IIT-Lab/Paper-with-Code-of-Wireless-communication-Based-on-DL/blob/master/English%20version.md).
  2 | 
  3 | 随着深度学习的发展，使用深度学习解决相关通信领域问题的研究也越来越多。作为一名通信专业的研究生，如果实验室没有相关方向的代码积累，入门并深入一个新的方向会十分艰难。同时，大部分通信领域的论文不会提供开源代码，reproducible research比较困难。
  4 | <br>
  5 | 基于深度学习的通信论文这几年飞速增加，明显能感觉这些论文的作者更具开源精神。本项目专注于整理在通信中应用深度学习，并公开了相关源代码的论文。
  6 | <br>
  7 | 个人关注的领域和精力有限，这个列表不会那么完整。如果你知道一些相关的开源论文，但不在此列表中，非常欢迎添加，为community贡献一份力量。欢迎交流^_^
  8 | <br>
  9 | **温馨提示:watch相较于star更容易得到更新通知 。**
 10 | <br>
 11 | TODO 
 12 | 
 13 | - [ ] 按不同小方向分类
 14 | - [ ] 论文添加下载链接
 15 | - [ ] 增加更多相关论文代码
 16 | - [ ] 传统通信论文代码列表
 17 | - [ ] “通信+DL”论文列表（引用较高，可以没有代码）
 18 | 
 19 | 
 20 | 
 21 | # 论文/Paper
 22 | 
 23 | 
 24 | | Paper                                                        | Code                                                         |
 25 | | ------------------------------------------------------------ | ------------------------------------------------------------ |
 26 | |[Deep Learning for SVD and Hybrid Beamforming](https://ieeexplore.ieee.org/abstract/document/9130130)|[DL_SVD_BF](https://www.dropbox.com/sh/v0gs7ba0qq5x168/AACyqRoCz5m3fhpF-azkbn3Qa?dl=0)|
 27 | |[Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison](https://arxiv.org/abs/2006.16015)|[Reverse-Jensen_MI_estimation](https://github.com/Fritschek/Reverse-Jensen_MI_estimation)|
 28 | |[Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems](https://arxiv.org/abs/1912.12265)|[Codes-for-Deep-Transfer-Learning-Based-Downlink-Channel-Prediction-for-FDD-Massive-MIMO-Systems](https://github.com/yangyuwenyang/Codes-for-Deep-Transfer-Learning-Based-Downlink-Channel-Prediction-for-FDD-Massive-MIMO-Systems)|
 29 | |[Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN](https://arxiv.org/abs/2006.11435)|[Channel_Estimation_cGAN](https://github.com/YudiDong/Channel_Estimation_cGAN)|
 30 | |A Model-Driven Deep Learning Method for Normalized Min-Sum LDPC Decoding|[A-Model-Driven-Deep-Learning-Method-for-Normalized-Min-Sum-LDPC-Decoding](https://github.com/tjuxiaofeng/A-Model-Driven-Deep-Learning-Method-for-Normalized-Min-Sum-LDPC-Decoding)|
 31 | |Complex-Valued Convolutions for Modulation Recognition using Deep Learning|[Complex_Convolutions](https://github.com/JakobKrzyston/Complex_Convolutions)|
 32 | |[Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning](https://arxiv.org/abs/1904.10136)|[LIS-DeepLearning](https://github.com/Abdelrahman-Taha/LIS-DeepLearning)|
 33 | |Wireless link adaptation - a hybrid data-driven and model-based approach|[LinkAdaptationCSI](https://github.com/lpkg/LinkAdaptationCSI)|
 34 | |Deep unfolding of the weighted MMSE algorithm|[WMMSE-deep-unfolding](https://github.com/lpkg/WMMSE-deep-unfolding)|
 35 | |[Learn to Compress CSI and Allocate Resources in Vehicular Networks](https://arxiv.org/abs/1908.04685)|[Learn-CompressCSI-RA-V2X-Code](https://github.com/CooperLWang/Learn-CompressCSI-RA-V2X-Code)|
 36 | |[Benchmarking End-to-end Learning of MIMO Physical-Layer Communication](https://arxiv.org/abs/2005.09718)|[DeepLearning_MIMO](https://github.com/JSChalmers/DeepLearning_MIMO)|
 37 | |[Learned Conjugate Gradient Descent Network for Massive MIMO Detection](https://arxiv.org/abs/1906.03814)|[LcgNet](https://github.com/YiWei0129/LcgNet)|
 38 | |[Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach](https://arxiv.org/abs/1812.10044)|[overloaded_MIMO](https://github.com/wadayama/overloaded_MIMO)|
 39 | |[Deep Soft Interference Cancellation for MIMO Detection](https://ieeexplore.ieee.org/document/9054732)|[DeepSIC](https://github.com/nirshlezinger1/DeepSIC)|
 40 | |[Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks](https://ieeexplore.ieee.org/document/8969641)|[AoI_RL](https://github.com/aelgabli/AoI_RL)|
 41 | |[Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design](https://arxiv.org/abs/1903.03128)|[Genetic-Algorithm-based-LDPC-Code-Design](https://github.com/AhmedElkelesh/Genetic-Algorithm-based-LDPC-Code-Design)|
 42 | |[MaMIMO CSI-based positioning using CNNs: Peeking inside the black box](https://arxiv.org/abs/2003.04581)|[inside-the-black-box](https://github.com/sibrendebast/inside-the-black-box)|
 43 | |[Learning Combinatorial Optimization Algorithms over Graphs](https://arxiv.org/abs/1704.01665)|[graph_comb_opt](https://github.com/Hanjun-Dai/graph_comb_opt.git)|
 44 | |[Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management](https://arxiv.org/abs/2002.12877)|[RNNASIP](https://github.com/andrire/RNNASIP)|
 45 | |[Power Allocation in Multi-user Cellular Networks With Deep Q Learning Approach](https://arxiv.org/abs/1812.02979)|[PA_ICC](https://github.com/mengxiaomao/PA_ICC)|
 46 | |[Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches](https://arxiv.org/abs/1901.07159)|[PA_TWC](https://github.com/mengxiaomao/PA_TWC)|
 47 | |[Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://arxiv.org/pdf/1910.13067.pdf)|[FEDL](https://github.com/nhatminh/FEDL)|
 48 | |[Federated Learning over Wireless Networks: Optimization Model Design and Analysis](https://ieeexplore.ieee.org/document/8737464)|[OnDevAI](https://github.com/nhatminh/OnDevAI)|
 49 | |[Deep learning based end-to-end wireless communication systems with conditional GAN as unknown channel](https://arxiv.org/pdf/1903.02551.pdf)|[End2End_GAN](https://github.com/haoyye/End2End_GAN)|
 50 | |[Intelligent Resource Allocation in Wireless Communications Systems](https://ieeexplore.ieee.org/document/8961912)|[IRAWCS](https://github.com/seotaijiya/IRAWCS)|
 51 | |[Spatio-Temporal Representation with Deep Recurrent Network in MIMO CSI Feedback](https://ieeexplore.ieee.org/document/8951228)|[ConvlstmCsiNet](https://github.com/Aries-LXY/ConvlstmCsiNet)|
 52 | |[Neural Network Aided SC Decoder for Polar Codes](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8780605)|[1_NND](https://github.com/BruceGaoo/1_NND)|
 53 | |[Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8638509)|[Bi-Directional-Channel-Reciprocity](https://github.com/DLinWL/Bi-Directional-Channel-Reciprocity)|
 54 | |[Performance Evaluation of Channel Decoding With Deep Neural Networks](https://arxiv.org/pdf/1711.00727.pdf)|[deep-neural-network-decoder](https://github.com/levylv/deep-neural-network-decoder)|
 55 | |[Learning the MMSE Channel Estimator](https://arxiv.org/pdf/1707.05674v3.pdf)|[learning-mmse-est](https://github.com/tum-msv/learning-mmse-est)|
 56 | |[Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications](https://ieeexplore.ieee.org/document/8731635)|[Energy-Harvesting-DDPG](https://github.com/CrQiu/Energy-Harvesting-DDPG-)|
 57 | |[Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding](https://arxiv.org/abs/1911.12410)|[deep1bitVAE](https://github.com/skhobahi/deep1bitVAE) *Not Yet*|
 58 | |[CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://arxiv.org/abs/1911.11523)|[MaMIMO_CSI_with_CNN_positioning](https://github.com/sibrendebast/MaMIMO_CSI_with_CNN_positioning)|
 59 | |[Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://arxiv.org/abs/1910.02900)|[Sub6-Preds-mmWave](https://github.com/malrabeiah/Sub6-Preds-mmWave)|
 60 | |[Deep Learning for Channel Coding via Neural Mutual Information Estimation](https://ieeexplore.ieee.org/document/8815464)|[Wireless_encoding_with_MI_estimation](https://github.com/Fritschek/Wireless_encoding_with_MI_estimation)|
 61 | |[Deep Learning for the Gaussian Wiretap Channel](https://ieeexplore.ieee.org/abstract/document/8761681)|[NN_GWTC](https://github.com/Fritschek/NN_GWTC)|
 62 | |[Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://arxiv.org/abs/1910.14322)|[CRNet](https://github.com/Kylin9511/CRNet) *Recommend! very detailed README* |
 63 | |[Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://ieeexplore.ieee.org/document/8665952)|[DLMA](https://github.com/YidingYu/DLMA)|
 64 | |[Mobility-Aware Centralized Reinforcement Learning for Dynamic Resource Allocation in HetNets](https://www.researchgate.net/publication/335159543_Mobility-Aware_Centralized_Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_HetNets)|[UARA](https://github.com/LiuJieShane/UARA)|
 65 | |[Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems](https://arxiv.org/abs/1905.13212)|[DL-hybrid-precoder](https://github.com/lxf8519/DL-hybrid-precoder)|
 66 | |[Deep Learning-Based Detector for OFDM-IM](https://ieeexplore.ieee.org/document/8684894)|[DeepIM](https://github.com/ThienVanLuong/DeepIM)|
 67 | |[Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels](https://arxiv.org/abs/1910.09945)|[meta-autoencoder](https://github.com/kclip/meta-autoencoder)|
 68 | |[Learning to Communicate in a Noisy Environment](https://arxiv.org/abs/1910.09630)|[echo](https://github.com/ml4wireless/echo)|
 69 | |[Low-rank mmWave MIMO channel estimation in one-bit receivers](https://arxiv.org/abs/1910.09141)|[Low-rank-MIMO-channel-estimation-from-one-bit-measurements](https://github.com/nitinjmyers/Low-rank-MIMO-channel-estimation-from-one-bit-measurements)|
 70 | |[Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots](https://arxiv.org/abs/1910.06960)|[1-Bit-ADCs](https://github.com/YuZhang-GitHub/1-Bit-ADCs)|
 71 | |[ns-3 meets OpenAI Gym: The Playground for Machine Learning in Networking Research](https://arxiv.org/pdf/1810.03943.pdf)|[ns3-gym](https://github.com/tkn-tub/ns3-gym)|
 72 | | Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels  | [yihanjiang](https://github.com/yihanjiang)/[turboae](https://github.com/yihanjiang/turboae) |
 73 | | Communication Algorithms via Deep Learning  | [yihanjiang](https://github.com/yihanjiang)/[commviadl](https://github.com/yihanjiang/Sequential-RNN-Decoder) |
 74 | |[Towards Optimal Power Control via Ensembling Deep Neural Networks](https://arxiv.org/abs/1807.10025)|[PCNet-ePCNet](https://github.com/ShenGroup/PCNet-ePCNet)|
 75 | |[Low-Precision Neural Network Decoding of Polar Codes](https://ieeexplore.ieee.org/abstract/document/8815542)|[low-precision-nnd](https://github.com/IgWod/low-precision-nnd)|
 76 | |[A Graph Neural Network Approach for Scalable Wireless Power Control](https://arxiv.org/pdf/1907.08487.pdf)|[Globecom2019](https://github.com/yshenaw/Globecom2019)|
 77 | |[CNN-based Precoder and Combiner Design in mmWave MIMO Systems](https://ieeexplore.ieee.org/document/8710287)|[Deep_HybridBeamforming](https://github.com/meuseabe/Deep_HybridBeamforming)|
 78 | |[Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification](https://arxiv.org/pdf/1909.03050.pdf)|[coming soon](https://github.com/kython)|
 79 | |[An Open-Source Framework for Adaptive Traffic Signal Control](https://arxiv.org/pdf/1909.00395.pdf)|[docwza/sumolights](https://github.com/docwza/sumolights)|
 80 | |[A CNN-Based End-to-End Learning Framework Towards Intelligent Communication Systems](https://ieeexplore.ieee.org/document/8755977)|[Deepcom](https://github.com/ZhangKaiyao/Deepcom)|
 81 | |[Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding](https://arxiv.org/pdf/1906.04448.pdf)|[RLdecoding](https://github.com/fabriziocarpi/RLdecoding)|
 82 | |[Adaptive Neural Signal Detection for Massive MIMO](https://arxiv.org/abs/1906.04610)|[mehrdadkhani/MMNet](https://github.com/mehrdadkhani/MMNet)|
 83 | |[Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://ieeexplore.ieee.org/document/8303773)|[DynamicMultiChannelRL](https://github.com/GulatiAditya/DynamicMultiChannelRL)|
 84 | | Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells|[Q-Learning-Power-Control](https://github.com/farismismar/Q-Learning-Power-Control)|
 85 | |[Spectrum sharing in vehicular networks based on multi-agent reinforcement learning](https://arxiv.org/abs/1905.02910)|[MARLspectrumSharingV2X](https://github.com/AlexVic/MARLspectrumSharingV2X)|
 86 | |[Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors](https://ieeexplore.ieee.org/abstract/document/8357902)|[modulation_classif](https://github.com/zeroXzero/modulation_classif)|
 87 | |Learning Physical-Layer Communication with Quantized Feedback|[quantizedfeedback](https://github.com/henkwymeersch/quantizedfeedback)|
 88 | |[Decentralized Scheduling for Cooperative Localization with Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8701533)|[DeepRLVehicularLocalization](https://github.com/henkwymeersch/DeepRLVehicularLocalization)|
 89 | |Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks|[DynamicMultiChannelRL](https://github.com/GulatiAditya/DynamicMultiChannelRL)|
 90 | |MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders|[MIST_CNN_Decoder](https://github.com/kryashashwi/MIST_CNN_Decoder)|
 91 | |[Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink](https://arxiv.org/abs/1812.07518)|[UL2DL](https://github.com/safarisadegh/UL2DL)|
 92 | |Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency|[DL-Massive-MIMO](https://github.com/malrabeiah/DL-Massive-MIMO)|
 93 | |Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks|[DPPL](https://github.com/stochastic-geometry/DPPL)|
 94 | |Learning Based Power Control for mmWave Massive MIMO against Jamming|[Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming](https://github.com/xiaozhch5/Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming)|
 95 | |Sparsely Connected Neural Network for Massive MIMO Detection|[MIMO_Detection](https://github.com/NobleLee/MIMO_Detection)|
 96 | |[Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learningg](https://ieeexplore.ieee.org/abstract/document/8690757)|[qfnet](https://github.com/kangcp/qfnet)|
 97 | |Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks|[DL-CoMP-Machine-Learning](https://github.com/farismismar/DL-CoMP-Machine-Learning)|
 98 | |Deep Reinforcement Learning for Resource Allocation in V2V Communications|https://github.com/haoyye/ResourceAllocationReinforcementLearning|
 99 | |RF-based Direction Finding of UAVs Using DNN|https://github.com/LahiruJayasinghe/DeepDOA|
100 | | Deepcode: Feedback Codes via Deep Learning                   | https://github.com/hyejikim1/Deepcode<br>https://github.com/yihanjiang/feedback_code |
101 | | Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems | https://github.com/meysamsadeghi/Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems |
102 | | [AIF: An Artificial Intelligence Framework for Smart Wireless Network Management](http://ieeexplore.ieee.org/document/8119495/metrics) | [caogang](https://github.com/caogang)/[WlanDqn](https://github.com/caogang/WlanDqn) |
103 | | Deep-Learning-Power-Allocation-in-Massive-MIMO               | [lucasanguinetti / Deep-Learning-Power-Allocation-in-Massive-MIMO](https://github.com/lucasanguinetti/Deep-Learning-Power-Allocation-in-Massive-MIMO) |
104 | | DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications | [The DeepMIMO Dataset](http://deepmimo.net/)                 |
105 | | Fast Deep Learning for Automatic Modulation Classification   | [dl4amc](https://github.com/dl4amc)/[source](https://github.com/dl4amc/source) |
106 | | Deep Learning-Based Channel Estimation                       | [Mehran-Soltani](https://github.com/Mehran-Soltani)/[ChannelNet](https://github.com/Mehran-Soltani/ChannelNet) |
107 | | Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication | [seotaijiya](https://github.com/seotaijiya)/[TPC_D2D](https://github.com/seotaijiya/TPC_D2D) |
108 | | Deep learning-based channel estimation for beamspace mmWave massive MIMO systems | [hehengtao](https://github.com/hehengtao)/[LDAMP_based-Channel-estimation](https://github.com/hehengtao/LDAMP_based-Channel-estimation) |
109 | | Spatial deep learning for wireless scheduling                | [willtop](https://github.com/willtop)/[Spatial_DeepLearning_Wireless_Scheduling](https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling) |
110 | | Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach | [swordest](https://github.com/swordest)/[mec_drl](https://github.com/swordest/mec_drl) |
111 | | A deep-reinforcement learning approach for software-defined networking routing optimization | [knowledgedefinednetworking / a-deep-rl-approach-for-sdn-routing-optimization](https://github.com/knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization) |
112 | | Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells | [farismismar / Q-Learning-Power-Control](https://github.com/farismismar/Q-Learning-Power-Control) |
113 | | Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks | [bmatthiesen / deep-EE-opt](https://github.com/bmatthiesen/deep-EE-opt) |
114 | | Actor-Critic-Based Resource Allocation for Multi-modal Optical Networks | [BoyuanYan / Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks](https://github.com/BoyuanYan/Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks) |
115 | | Deep MIMO Detection                                          | [neevsamuel](https://github.com/neevsamuel)/[DeepMIMODetection](https://github.com/neevsamuel/DeepMIMODetection) |
116 | | Learning to Detect                                           | [neevsamuel](https://github.com/neevsamuel)/[LearningToDetect](https://github.com/neevsamuel/LearningToDetect) |
117 | | An iterative BP-CNN architecture for channel decoding        | [liangfei-info](https://github.com/liangfei-info)/[Iterative-BP-CNN](https://github.com/liangfei-info/Iterative-BP-CNN) |
118 | | On Deep Learning-Based Channel Decoding                      | [gruberto/DL-ChannelDecoding](https://github.com/gruberto/DL-ChannelDecoding) <br/>[Decoder-using-deep-learning](https://github.com/VivekRamalingamK/Decoder-using-deep-learning)|
119 | | DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls | [ruihuili / DELMU](https://github.com/ruihuili/DELMU)        |
120 | | Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement | [farismismar / Deep-Q-Learning-SON-Perf-Improvement](https://github.com/farismismar/Deep-Q-Learning-SON-Perf-Improvement) |
121 | | An Introduction to Deep Learning for the Physical Layer      | [yashcao / RTN-DL-for-physical-layer](https://github.com/yashcao/RTN-DL-for-physical-layer)<br />[musicbeer / Deep-Learning-for-the-Physical-Layer](https://github.com/musicbeer/Deep-Learning-for-the-Physical-Layer)<br />[helloMRDJ / autoencoder-for-the-Physical-Layer](https://github.com/helloMRDJ/autoencoder-for-the-Physical-Layer) |
122 | | Convolutional Radio Modulation Recognition Networks          | [chrisruk](https://github.com/chrisruk)/[cnn](https://github.com/chrisruk/cnn)<br />[qieaaa / Singal-CNN](https://github.com/qieaaa/Singal-CNN) |
123 | | Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks | [zhongyuanzhao / dl_ofdm](https://github.com/zhongyuanzhao/dl_ofdm) |
124 | | Joint Transceiver Optimization for WirelessCommunication PHY with Convolutional NeuralNetwork | [hlz1992/RadioCNN](https://github.com/hlz1992/RadioCNN)      |
125 | | Deep Learning for Massive MIMO CSI Feedback                  | [sydney222 / Python_CsiNet](https://github.com/sydney222/Python_CsiNet) |
126 | | [Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning](http://arxiv.org/abs/1904.03657) | [TianLin0509/BF-design-with-DL](https://github.com/TianLin0509/BF-design-with-DL)|
127 | | 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning | [lasseufpa](https://github.com/lasseufpa)/[5gm-data](https://github.com/lasseufpa/5gm-data) |
128 | | Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks | [shkrwnd](https://github.com/shkrwnd)/[Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access](https://github.com/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access) |
129 | | DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning | [zaxliu](https://github.com/zaxliu)/[deepnap](https://github.com/zaxliu/deepnap) |
130 | | Automatic Modulation Classification: A Deep Learning Enabled Approach | [mengxiaomao](https://github.com/mengxiaomao)/[CNN_AMC](https://github.com/mengxiaomao/CNN_AMC) |
131 | | Deep Architectures for Modulation Recognition                | [qieaaa / Deep-Architectures-for-Modulation-Recognition](https://github.com/qieaaa/Deep-Architectures-for-Modulation-Recognition) |
132 | | Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks | [mkoz71 / Energy-Efficiency-in-Reinforcement-Learning](https://github.com/mkoz71/Energy-Efficiency-in-Reinforcement-Learning) |
133 | | Learning to optimize: Training deep neural networks for wireless resource management | [Haoran-S / DNN_WMMSE](https://github.com/Haoran-S/DNN_WMMSE) |
134 | | Implications of Decentralized Q-learning Resource Allocation in Wireless Networks | [wn-upf / decentralized_qlearning_resource_allocation_in_wns](https://github.com/wn-upf/decentralized_qlearning_resource_allocation_in_wns) |
135 | | Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems | [haoyye/OFDM_DNN](https://github.com/haoyye/OFDM_DNN)        |
136 | 
137 | # "通信+DL"论文（无代码)/Paper List Without Code
138 | 说明：论文主要来源于arxiv中[Signal Processing](https://arxiv.org/list/eess.SP/recent)和[Information Theory](https://arxiv.org/list/cs.IT/recent)
139 | * [Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach](https://arxiv.org/pdf/1903.12546.pdf)
140 | * [Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1904.00601.pdf)
141 | * [Machine Learning for Wireless Communication Channel Modeling: An Overview](https://link.springer.com/article/10.1007/s11277-019-06275-4)
142 | * [Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning](https://arxiv.org/pdf/1903.08163.pdf)
143 | 
144 | # 数据集/Database
145 | * [MASSIVE MIMO CSI MEASUREMENTS](https://homes.esat.kuleuven.be/~sdebast/csi_measurements.html)
146 | * [SM-CsiNet+ and PM-CsiNet+](https://drive.google.com/drive/folders/1_lAMLk_5k1Z8zJQlTr5NRnSD6ACaNRtj?usp=sharing):来自论文[Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis](https://arxiv.org/pdf/1906.06007.pdf)
147 | * [An open online real modulated dataset](https://pan.baidu.com/s/1biDooH6E81Toxa2u4D3p2g):来自论文[Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics](https://arxiv.org/pdf/1903.04297.pdf)。
148 | > To the best of our knowledge,this is the first open dataset of real modulated signals
149 | > for wireless communication systems.
150 | * [RF DATASETS FOR MACHINE LEARNING](https://www.deepsig.io/datasets)
151 | * [open datase](https://pan.baidu.com/s/1rS143bEDaOTEiCneXE67dg#list/path=%2F):来自论文[Signal Demodulation With Machine Learning
152 | Methods for Physical Layer Visible Light
153 | Communications: Prototype Platform,
154 | Open Dataset, and Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8661606&tag=1)
155 | >The dataset is collected in real physical environment, and the channel suffers from many factors such as limited LED bandwidth, multi-reflection,spurious or continuous jamming, etc.
156 | # 学者个人主页/Researcher Homepage
157 | * [Ahmed Alkhateeb](http://www.aalkhateeb.net/index.html):Research Interests
158 |   - Millimeter Wave and Massive MIMO Communication
159 |   - Vehicular and Drone Communication Systems
160 |   - Applications of Machine Learning in Wireless Communication
161 |   - Building Mobile Communication Systems that Work in Reality!
162 | * [Emil Björnson](https://ebjornson.com/research/):
163 |   He performs research on multi-antenna communications, Massive MIMO, radio resource allocation, energy-efficient communications, and network design. 
164 | * [Leo-Chu](https://github.com/Leo-Chu):His research interests are in the theoretical and algorithmic studies in random matrix theory, nonconvex optimization, deep learning, as well as their applications in wireless communications, bioengineering, and smart grid.
165 | # 有用的网页和材料/Useful Websites and Materials
166 | * [机器学习和通信结合论文列表/Research Library ](https://mlc.committees.comsoc.org/research-library/)
167 | * [Best Readings in Machine Learning in Communications](https://www.comsoc.org/publications/best-readings/machine-learning-communications)
168 | 
169 | 
170 | <br>贡献者/Contributors：
171 | 
172 | * WxZhu:
173 |   - [Github](https://github.com/zhuwenxing)  
174 |   - Email:wenxingzhu@shu.edu.cn
175 | * [LinTian](https://github.com/TianLin0509)
176 | * [HongtaiChen](https://github.com/HongtaiChen)
177 | * [yihanjiang](https://github.com/yihanjiang)
178 | * wu huaming:
179 |   - Email:whming@tju.edu.cn
180 | 
181 | <br>版本更新/Version Update：
182 | 
183 | 1. 第一版完成/First Version：2019-02-21
184 | 


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