├── .gitattributes ├── English version.md └── README.md /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /English version.md: -------------------------------------------------------------------------------- 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.
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 .
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)
[musicbeer / Deep-Learning-for-the-Physical-Layer](https://github.com/musicbeer/Deep-Learning-for-the-Physical-Layer)
[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)
[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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 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 |
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 |
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，非常欢迎添加在issue当中**，为community贡献一份力量。欢迎交流^_^ 8 |
9 | **温馨提示:watch相较于star更容易得到更新通知 。** 10 |
11 | TODO 12 | 13 | - [x] 按不同小方向分类 14 | - [x] 论文添加下载链接 15 | - [x] 增加更多相关论文代码 16 | * 在[daily_arxiv](https://github.com/zhuwenxing/daily_arxiv)这个repo下会以daily为尺度更新`eess.SP`和`cs.IT`分类下开源的代码论文 17 | * 该Repo通过爬虫+Github Action实现每日自动更新 18 | - [ ] 传统通信论文代码列表 19 | - [ ] “通信+DL”论文列表（引用较高，可以没有代码） 20 | 21 | 22 | ## 目录 （Contents) 23 | 24 | - [Topics](#topics) 25 | + [Machine/deep learning for physical layer optimization](#physical-layer-optimization) 26 | + [Resource, power and network optimization using machine learning techniques](#resource-and-network-optimization) 27 | + [Distributed learning algorithms over communication networks](#distributed-learning-algorithms-over-communication-networks) 28 | + [Multiple access scheduling and routing using machine learning techniques](#multiple-access-scheduling--and-routing-using-machine-learning-techniques) 29 | + [Machine learning for network slicing, network virtualization, and software-defined networking](#machine-learning-for--software-defined-networking) 30 | + [Machine learning for emerging communication systems and applications (e.g., IoT, edge computing, caching, smart cities, vehicular networks, and localization)](#machine-learning-for-emerging-communication-systems-and-applications) 31 | + [Secure machine learning over communication networks](#secure-machine-learning-over-communication-networks) 32 | 33 | 34 | 35 | 36 | ## Topics 37 | 38 | ### Physical layer optimization 39 | 40 | | Paper | Code | 41 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 42 | |[Online Meta-Learning For Hybrid Model-Based Deep Receivers](https://arxiv.org/abs/2203.14359)|[meta-deepsic](https://github.com/tomerraviv95/meta-deepsic)| 43 | |[Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access](https://arxiv.org/abs/2204.01731)|[jadce](https://github.com/deeeeeeplearning/jadce)| 44 | |[sionna: an open-source library for next-generation physical layer research](https://arxiv.org/abs/2203.11854)|[sionna](https://github.com/nvlabs/sionna)| 45 | |[Deep Learning Aided Robust Joint Channel Classification, Channel Estimation, and Signal Detection for Underwater Optical Communication](https://ieeexplore.ieee.org/document/9302692)|[UWOC-JCCESD](https://github.com/Huaiyin-Lu/UWOC-JCCESD)| 46 | |[LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers](https://arxiv.org/abs/2102.02993)|[LoRD-Net](https://github.com/skhobahi/LoRD-Net)| 47 | |[Deep Diffusion Models for Robust Channel Estimation](https://arxiv.org/abs/2111.08177)|[diffusion-channels](https://github.com/utcsilab/diffusion-channels)| 48 | |[A Channel Coding Benchmark for Meta-Learning](https://openreview.net/forum?id=DjzPaX8AT0z)|[MetaCC](https://github.com/ruihuili/MetaCC)| 49 | |[On the Feasibility of Modeling OFDM Communication Signals with Unsupervised Generative Adversarial Networks](https://arxiv.org/abs/2109.05107)|[OFDM-GAN](https://github.com/usnistgov/OFDM-GAN)| 50 | |[Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals](https://ieeexplore.ieee.org/document/9013332)|[LearningML](https://github.com/Yunseong-Cho/LearningML)| 51 | |[iterative error decimation for syndrome-based neural network decoders](https://arxiv.org/abs/2012.00089)|[ied](https://github.com/kamassury/ied)| 52 | |[ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning](https://arxiv.org/abs/2108.12920)|[kocodes](https://github.com/deepcomm/kocodes)| 53 | |[Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications](https://arxiv.org/abs/2009.01423)|[CDRN-channel-estimation-IRS](https://github.com/XML124/CDRN-channel-estimation-IRS)| 54 | |[Model-Driven Deep Learning for MIMO Detection](https://ieeexplore.ieee.org/document/9018199)|[OAMP-Net](https://github.com/hehengtao/OAMP-Net)| 55 | |[Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems](https://arxiv.org/abs/2106.04043)|[DCRNet](https://github.com/recusant7/DCRNet)| 56 | |[Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://arxiv.org/abs/2007.00038)|[HBF-Net](https://github.com/HamedHojatian/HBF-Net)| 57 | |[CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://arxiv.org/abs/2102.07507)|[CLNet](https://github.com/SIJIEJI/CLNet)| 58 | |[Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation](https://arxiv.org/abs/2008.03612)|[B_DNN](https://github.com/hasanabs/B_DNN)| 59 | |[Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment](https://arxiv.org/abs/2012.13607)|[DL-ActiveLearning-BeamAlignment](https://github.com/foadsohrabi/DL-ActiveLearning-BeamAlignment)| 60 | |[Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO](https://ieeexplore.ieee.org/document/9037126)|[Source-Code-X.Ma](https://github.com/gaozhen16/Source-Code-X.Ma)| 61 | |[Deep Learning Predictive Band Switching in Wireless Networks](https://arxiv.org/abs/1910.05305)|[Bandswitch-DeepMIMO](https://github.com/farismismar/Bandswitch-DeepMIMO)| 62 | |[RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://arxiv.org/abs/2007.00140)|[RE-MIMO](https://github.com/krpratik/RE-MIMO)| 63 | |[NOLD: A Neural-Network Optimized Low-Resolution Decoder for LDPC Codes](https://github.com/Leo-Chu/NOLD/blob/master/JCN20-DIV2-067.R2.pdf)|[NOLD](https://github.com/Leo-Chu/NOLD)| 64 | |[A MIMO detector with deep learning in the presence of correlated interference](https://ieeexplore.ieee.org/abstract/document/8990045)|[project_dcnnmld](https://github.com/skypitcher/project_dcnnmld)| 65 | |[Deep Learning Driven Non-Orthogonal Precoding for Millimeter Wave Communications](https://ieeexplore.ieee.org/document/9082619)|[Deep-Learning-Driven-Non-Orthogonal-Precoding-for-Millimeter-Wave-Communications](https://github.com/JKLinUESTC/Deep-Learning-Driven-Non-Orthogonal-Precoding-for-Millimeter-Wave-Communications)| 66 | |[Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9246287)|[DeepUnfolding_WMMSE](https://github.com/hqyyqh888/DeepUnfolding_WMMSE)| 67 | | [Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems](https://arxiv.org/pdf/1708.08514.pdf)| [haoyye/OFDM_DNN](https://github.com/haoyye/OFDM_DNN) | 68 | | [Automatic Modulation Classification: A Deep Learning Enabled Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8454504) | [mengxiaomao](https://github.com/mengxiaomao)/[CNN_AMC](https://github.com/mengxiaomao/CNN_AMC) | 69 | | [Deep Architectures for Modulation Recognition](https://arxiv.org/pdf/1703.09197.pdf) | [qieaaa / Deep-Architectures-for-Modulation-Recognition](https://github.com/qieaaa/Deep-Architectures-for-Modulation-Recognition) | 70 | | [Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networkss](https://ieeexplore.ieee.org/document/9448141) | [zhongyuanzhao / dl_ofdm](https://github.com/zhongyuanzhao/dl_ofdm) | 71 | | [Joint Transceiver Optimization for Wireless Communication PHY with Convolutional NeuralNetwork](https://arxiv.org/abs/1808.03242) | [hlz1992/RadioCNN](https://github.com/hlz1992/RadioCNN) | 72 | | [5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning](https://par.nsf.gov/servlets/purl/10112564)| [lasseufpa](https://github.com/lasseufpa)/[5gm-data](https://github.com/lasseufpa/5gm-data) | 73 | |[A Two-Fold Group Lasso Based Lightweight Deep Neural Network for Automatic Modulation Classification](https://ieeexplore.ieee.org/abstract/document/9145050)|[Group-Sparse-DNN-for-AMC](https://github.com/tjuxiaofeng/Group-Sparse-DNN-for-AMC)| 74 | |[Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification](https://arxiv.org/abs/2009.13560)|[MultiStage-Grassmannian-DNN](https://github.com/StefanSchwarzTUW/MultiStage-Grassmannian-DNN)| 75 | | [Deep Learning for Massive MIMO CSI Feedback](https://arxiv.org/pdf/1712.08919.pdf) | [sydney222 / Python_CsiNet](https://github.com/sydney222/Python_CsiNet) | 76 | | [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)| 77 | | [An Introduction to Deep Learning for the Physical Layer](https://arxiv.org/pdf/1702.00832.pdf) | [yashcao / RTN-DL-for-physical-layer](https://github.com/yashcao/RTN-DL-for-physical-layer)
[musicbeer / Deep-Learning-for-the-Physical-Layer](https://github.com/musicbeer/Deep-Learning-for-the-Physical-Layer)
[helloMRDJ / autoencoder-for-the-Physical-Layer](https://github.com/helloMRDJ/autoencoder-for-the-Physical-Layer)| 78 | | [Deep MIMO Detection](https://arxiv.org/pdf/1706.01151.pdf) | [neevsamuel](https://github.com/neevsamuel)/[DeepMIMODetection](https://github.com/neevsamuel/DeepMIMODetection) | 79 | | [Learning to Detect](https://arxiv.org/pdf/1805.07631.pdf) | [neevsamuel](https://github.com/neevsamuel)/[LearningToDetect](https://github.com/neevsamuel/LearningToDetect) | 80 | | [An iterative BP-CNN architecture for channel decoding](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8259241) | [liangfei-info](https://github.com/liangfei-info)/[Iterative-BP-CNN](https://github.com/liangfei-info/Iterative-BP-CNN) | 81 | | [On Deep Learning-Based Channel Decoding](https://arxiv.org/pdf/1701.07738.pdf)| [gruberto/DL-ChannelDecoding](https://github.com/gruberto/DL-ChannelDecoding)
[Decoder-using-deep-learning](https://github.com/VivekRamalingamK/Decoder-using-deep-learning)| 82 | | [Deep learning-based channel estimation for beamspace mmWave massive MIMO systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8353153)| [hehengtao](https://github.com/hehengtao)/[LDAMP_based-Channel-estimation](https://github.com/hehengtao/LDAMP_based-Channel-estimation) | 83 | | [Fast Deep Learning for Automatic Modulation Classification](https://arxiv.org/pdf/1901.05850.pdf) | [dl4amc](https://github.com/dl4amc)/[source](https://github.com/dl4amc/source) | 84 | | [Deep Learning-Based Channel Estimation](https://arxiv.org/pdf/1810.05893.pdf)| [Mehran-Soltani](https://github.com/Mehran-Soltani)/[ChannelNet](https://github.com/Mehran-Soltani/ChannelNet) | 85 | |[Sparsely Connected Neural Network for Massive MIMO Detection](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8780959)|[MIMO_Detection](https://github.com/NobleLee/MIMO_Detection)| 86 | | [Deepcode: Feedback Codes via Deep Learning](https://arxiv.org/pdf/1807.00801.pdf) | https://github.com/hyejikim1/Deepcode
https://github.com/yihanjiang/feedback_code | 87 | |[MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders](https://arxiv.org/pdf/1905.08990.pdf)|[MIST_CNN_Decoder](https://github.com/kryashashwi/MIST_CNN_Decoder)| 88 | |[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)| 89 | |[Learning Physical-Layer Communication with Quantized Feedback](https://arxiv.org/pdf/1904.09252.pdf)|[quantizedfeedback](https://github.com/henkwymeersch/quantizedfeedback)| 90 | |[Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding](https://arxiv.org/pdf/1906.04448.pdf)|[RLdecoding](https://github.com/fabriziocarpi/RLdecoding)| 91 | |[Adaptive Neural Signal Detection for Massive MIMO](https://arxiv.org/abs/1906.04610)|[mehrdadkhani/MMNet](https://github.com/mehrdadkhani/MMNet)| 92 | |[CNN-based Precoder and Combiner Design in mmWave MIMO Systems](https://ieeexplore.ieee.org/document/8710287)|[Deep_HybridBeamforming](https://github.com/meuseabe/Deep_HybridBeamforming)| 93 | |[Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification](https://arxiv.org/pdf/1909.03050.pdf)|[coming soon](https://github.com/kython)| 94 | |[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)| 95 | |[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)| 96 | |[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)| 97 | |[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)| 98 | |[Deep Learning-Based Detector for OFDM-IM](https://ieeexplore.ieee.org/document/8684894)|[DeepIM](https://github.com/ThienVanLuong/DeepIM)| 99 | |[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)| 100 | |[Learning the MMSE Channel Estimator](https://arxiv.org/pdf/1707.05674v3.pdf)|[learning-mmse-est](https://github.com/tum-msv/learning-mmse-est)| 101 | |[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)| 102 | |[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)| 103 | |[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)| 104 | |[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)| 105 | |[Benchmarking End-to-end Learning of MIMO Physical-Layer Communication](https://arxiv.org/abs/2005.09718)|[DeepLearning_MIMO](https://github.com/JSChalmers/DeepLearning_MIMO)| 106 | |[Learned Conjugate Gradient Descent Network for Massive MIMO Detection](https://arxiv.org/abs/1906.03814)|[LcgNet](https://github.com/YiWei0129/LcgNet)| 107 | |[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)| 108 | |[Deep Soft Interference Cancellation for MIMO Detection](https://ieeexplore.ieee.org/document/9054732)|[DeepSIC](https://github.com/nirshlezinger1/DeepSIC)| 109 | |[Deep unfolding of the weighted MMSE algorithm](https://arxiv.org/pdf/2006.08448.pdf)|[WMMSE-deep-unfolding](https://github.com/lpkg/WMMSE-deep-unfolding)| 110 | |[Deep Learning for Direction of Arrival Estimation via Emulation of Large Antenna Arrays](https://arxiv.org/abs/2007.13824)|[DoA with DNN via Emulation of Antenna Arrays](https://gitlab.com/miriyugl/doa-with-dnn-via-emulation-of-antenna-arrays)| 111 | |[Acquiring Measurement Matrices via Deep Basis Persuit for Sparse Channel Estimation in mmWave Massive MIMO Systems](https://arxiv.org/abs/2007.05177)|[DeepBP-AE](https://github.com/Pengxia-Wu/DeepBP-AE)| 112 | |[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)| 113 | |[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)| 114 | |[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)| 115 | |[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)| 116 | |[A Model-Driven Deep Learning Method for Normalized Min-Sum LDPC Decoding](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9145237)|[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)| 117 | |[Complex-Valued Convolutions for Modulation Recognition using Deep Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9145469)|[Complex_Convolutions](https://github.com/JakobKrzyston/Complex_Convolutions)| 118 | |[Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems](https://arxiv.org/abs/1808.02208)|[GAN-cov-matrix](https://github.com/lxf8519/GAN-cov-matrix)| 119 | |[Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems](https://ieeexplore.ieee.org/document/9207745)|[Simulation Codes](http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.html)| 120 | |[Deep Learning for Polar Codes over Flat Fading Channels](https://ieeexplore.ieee.org/document/8669025)|[polarOverFlatFading](https://github.com/ade-irawan/polarOverFlatFading)| 121 | |[Aggregated Network for Massive MIMO CSI Feedback](https://arxiv.org/abs/2101.06618)|[ACRNet](https://github.com/Kylin9511/ACRNet)| 122 | | [Convolutional Radio Modulation Recognition Networks](https://arxiv.org/pdf/1602.04105.pdf) | [chrisruk](https://github.com/chrisruk)/[cnn](https://github.com/chrisruk/cnn)
[qieaaa / Singal-CNN](https://github.com/qieaaa/Singal-CNN) | 123 | | [Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels](https://arxiv.org/pdf/1911.03038.pdf) | [yihanjiang](https://github.com/yihanjiang)/[turboae](https://github.com/yihanjiang/turboae) | 124 | |[Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://arxiv.org/abs/1910.14322)|[CRNet](https://github.com/Kylin9511/CRNet)| 125 | |[Spatio-Temporal Representation with Deep Recurrent Network in MIMO CSI Feedback](https://ieeexplore.ieee.org/document/8951228)|[ConvlstmCsiNet](https://github.com/Aries-LXY/ConvlstmCsiNet)| 126 | |[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)| 127 | |[Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency](https://arxiv.org/pdf/1905.03761.pdf)|[DL-Massive-MIMO](https://github.com/malrabeiah/DL-Massive-MIMO)| 128 | |[Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink](https://arxiv.org/abs/1812.07518)|[UL2DL](https://github.com/safarisadegh/UL2DL)| 129 | |[Towards Optimally Efficient Tree Search with Deep Temporal Difference Learning](https://arxiv.org/abs/2101.02420)|[hats](https://github.com/skypitcher/hats)| 130 | |[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)| 131 | |[A CNN-Based End-to-End Learning Framework Towards Intelligent Communication Systems](https://ieeexplore.ieee.org/document/8755977)|[Deepcom](https://github.com/ZhangKaiyao/Deepcom)| 132 | | [Communication Algorithms via Deep Learning](https://arxiv.org/abs/1805.09317) | [yihanjiang](https://github.com/yihanjiang)/[commviadl](https://github.com/yihanjiang/Sequential-RNN-Decoder) | 133 | |[Learning to Communicate in a Noisy Environment](https://arxiv.org/abs/1910.09630)|[echo](https://github.com/ml4wireless/echo)| 134 | |[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)| 135 | |[Deep energy autoencoder for noncoherent multicarrier MU-SIMO systems](https://ieeexplore.ieee.org/document/9036067)|[energy_autoencoder](https://github.com/ThienVanLuong/energy_autoencoder)| 136 | |[Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://arxiv.org/abs/2001.11085)|[deepChannelLearning4RIS](https://github.com/meuseabe/deepChannelLearning4RIS)| 137 | |[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)| 138 | |[RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://arxiv.org/abs/1911.09002)|[RadioUNet](https://github.com/RonLevie/RadioUNet)| 139 | |[Deep learning aided multicarrier systems](https://ieeexplore.ieee.org/abstract/document/9271932)|[multicarrier_autoencoder](https://github.com/ThienVanLuong/multicarrier_autoencoder)| 140 | 141 | ### Resource and network optimization 142 | | Paper | Code | 143 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 144 | |[Resource Allocation based on Graph Neural Networks in Vehicular Communications](https://ieeexplore.ieee.org/document/9322537)|[Globecom2020-ResourceAllocationGNN](https://github.com/Coolzyh/Globecom2020-ResourceAllocationGNN)| 145 | |[An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks](https://arxiv.org/abs/2201.08994)|[USRMNet-HWGCN](https://github.com/soulven/usrmnet-hwgcn)| 146 | |[Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://arxiv.org/abs/2111.07480)|[WirelessFL-PDGNet](https://github.com/bl166/wirelessfl-pdgnet)| 147 | |[Delay-Oriented Distributed Scheduling Using Graph Neural Networks](https://arxiv.org/abs/2111.07017)|[gcn-dql](https://github.com/zhongyuanzhao/gcn-dql)| 148 | |[Deep Learning Based MAC via Joint Channel Access and Rate Adaptation](https://arxiv.org/abs/2106.10307)|[Wireless-Signal-Strength-on-2.4GHz-WSS24-dataset](https://github.com/postman511/Wireless-Signal-Strength-on-2.4GHz-WSS24-dataset)| 149 | |[wireless link scheduling via graph representation learning: a comparative study of different supervision levels](https://arxiv.org/abs/2110.01722)|[LinkSchedulingGNNs_SupervisionStudy](https://github.com/navid-naderi/LinkSchedulingGNNs_SupervisionStudy)| 150 | |[Distributed Scheduling using Graph Neural Networks](https://arxiv.org/abs/2011.09430)|[distgcn](https://github.com/zhongyuanzhao/distgcn)| 151 | |[DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks](https://arxiv.org/abs/2012.14350)|[deepbeam](https://github.com/wineslab/deepbeam)| 152 | |[Graph Embedding-Based Wireless Link Scheduling With Few Training Samples](https://arxiv.org/abs/1906.02871)|[graph_embedding_link_scheduling](https://github.com/mengyuan-lee/graph_embedding_link_scheduling)| 153 | | [Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks](https://arxiv.org/pdf/1812.02538.pdf)| [mkoz71 / Energy-Efficiency-in-Reinforcement-Learning](https://github.com/mkoz71/Energy-Efficiency-in-Reinforcement-Learning) | 154 | | [Learning to optimize: Training deep neural networks for wireless resource management](https://arxiv.org/abs/1705.09412)| [Haoran-S / DNN_WMMSE](https://github.com/Haoran-S/DNN_WMMSE) | 155 | | [Implications of Decentralized Q-learning Resource Allocation in Wireless Networks](https://arxiv.org/pdf/1705.10508.pdf) | [wn-upf / decentralized_qlearning_resource_allocation_in_wns](https://github.com/wn-upf/decentralized_qlearning_resource_allocation_in_wns) | 156 | | [Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement](https://arxiv.org/abs/1707.02329) | [farismismar / Deep-Q-Learning-SON-Perf-Improvement](https://github.com/farismismar/Deep-Q-Learning-SON-Perf-Improvement) | 157 | | [Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells](https://arxiv.org/pdf/1707.03269.pdf) | [farismismar / Q-Learning-Power-Control](https://github.com/farismismar/Q-Learning-Power-Control) | 158 | | [Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks](https://arxiv.org/pdf/1812.06920.pdf) | [bmatthiesen / deep-EE-opt](https://github.com/bmatthiesen/deep-EE-opt) | 159 | | [Actor-Critic-Based Resource Allocation for Multi-modal Optical Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8644190) | [BoyuanYan / Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks](https://github.com/BoyuanYan/Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks)| 160 | | [Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication](https://ieeexplore.ieee.org/document/8428396) | [seotaijiya](https://github.com/seotaijiya)/[TPC_D2D](https://github.com/seotaijiya/TPC_D2D) | 161 | |[Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8690757)|[qfnet](https://github.com/kangcp/qfnet)| 162 | |[Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks](https://arxiv.org/pdf/1812.03421.pdf)|[DL-CoMP-Machine-Learning](https://github.com/farismismar/DL-CoMP-Machine-Learning)| 163 | |[Deep Reinforcement Learning for Resource Allocation in V2V Communications](https://arxiv.org/abs/1711.00968)|https://github.com/haoyye/ResourceAllocationReinforcementLearning| 164 | | [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) | 165 | | [Deep-Learning-Power-Allocation-in-Massive-MIMO](https://arxiv.org/abs/1812.03640) | [lucasanguinetti / Deep-Learning-Power-Allocation-in-Massive-MIMO](https://github.com/lucasanguinetti/Deep-Learning-Power-Allocation-in-Massive-MIMO) | 166 | |[Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks](https://arxiv.org/abs/1905.00504)|[DPPL](https://github.com/stochastic-geometry/DPPL)| 167 | |[Learning Based Power Control for mmWave Massive MIMO against Jamming](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8647173)|[Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming](https://github.com/xiaozhch5/Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming)| 168 | |[Towards Optimal Power Control via Ensembling Deep Neural Networks](https://arxiv.org/abs/1807.10025)|[PCNet-ePCNet](https://github.com/ShenGroup/PCNet-ePCNet)| 169 | |[A Graph Neural Network Approach for Scalable Wireless Power Control](https://arxiv.org/pdf/1907.08487.pdf)|[Globecom2019](https://github.com/yshenaw/Globecom2019)| 170 | |[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)| 171 | |[Intelligent Resource Allocation in Wireless Communications Systems](https://ieeexplore.ieee.org/document/8961912)|[IRAWCS](https://github.com/seotaijiya/IRAWCS)| 172 | |[Learning Combinatorial Optimization Algorithms over Graphs](https://arxiv.org/abs/1704.01665)|[graph_comb_opt](https://github.com/Hanjun-Dai/graph_comb_opt.git)| 173 | |[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)| 174 | |[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)| 175 | |[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)| 176 | |[Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation](https://arxiv.org/abs/2009.10812)|[Unrolled-WMMSE](https://github.com/ArCho48/Unrolled-WMMSE)| 177 | |[Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks](https://arxiv.org/abs/2009.06681)|[Power-Control-asilomar](https://github.com/sinannasir/Power-Control-asilomar)| 178 | |[Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://arxiv.org/abs/2007.07632)|[GNN-Resource-Management](https://github.com/yshenaw/GNN-Resource-Management)| 179 | |[Contrastive Self-Supervised Learning for Wireless Power Control](https://arxiv.org/abs/2010.11909)|[ContrastiveSSL_WirelessPowerControl](https://github.com/navid-naderi/ContrastiveSSL_WirelessPowerControl)| 180 | |[No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks](https://github.com/zhiguo-ding/CRNOMA_DDPG/blob/main/paper.pdf)|[CRNOMA_DDPG](https://github.com/zhiguo-ding/CRNOMA_DDPG)| 181 | |[Multicell Power Control under Rate Constraints with Deep Learning](https://arxiv.org/abs/2012.03655)|[SRnet-and-SRNet-Heu-for-power-control](https://github.com/Leeyyhh/SRnet-and-SRNet-Heu-for-power-control)| 182 | |[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)| 183 | |[Wireless link adaptation - a hybrid data-driven and model-based approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9154263)|[LinkAdaptationCSI](https://github.com/lpkg/LinkAdaptationCSI)| 184 | |[Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment](https://arxiv.org/abs/2011.07782)|[ICASSP2021](https://github.com/Haoran-S/ICASSP2021)| 185 | | [DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning](http://network.ee.tsinghua.edu.cn/niulab/wp-content/uploads/2018/10/deepnap_CCN.pdf) | [zaxliu](https://github.com/zaxliu)/[deepnap](https://github.com/zaxliu/deepnap) | 186 | |[No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks](https://arxiv.org/pdf/2104.06007.pdf)|[CRNOMA_DDPG](https://github.com/zhiguo-ding/CRNOMA_DDPG)| 187 | 188 | ### Distributed learning algorithms over communication networks 189 | | Paper | Code | 190 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 191 | |[A Scalable Federated Multi-agent Architecture for Networked Connected Communication Network](https://arxiv.org/abs/2108.00506)|[Fed-MF-MAL](https://github.com/paperflight/Fed-MF-MAL)| 192 | |[Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach](https://arxiv.org/abs/2011.10282)|[RIS-FL](https://github.com/liuhang1994/RIS-FL)| 193 | |[Decentralized Statistical Inference with Unrolled Graph Neural Networks](https://arxiv.org/abs/2104.01555)|[Learning-based-DOP-Framework](https://github.com/IrisWangHe/Learning-based-DOP-Framework)| 194 | |[Decentralized Scheduling for Cooperative Localization with Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8701533)|[DeepRLVehicularLocalization](https://github.com/henkwymeersch/DeepRLVehicularLocalization)| 195 | |[Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination](https://ieeexplore.ieee.org/abstract/document/9123956)|[DRL_for_DDBC](https://github.com/JungangGe/DRL_for_DDBC)| 196 | | [Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://arxiv.org/abs/1812.07394) | [swordest](https://github.com/swordest)/[mec_drl](https://github.com/swordest/mec_drl) | 197 | |[Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://arxiv.org/pdf/1910.13067.pdf)|[FEDL](https://github.com/nhatminh/FEDL)| 198 | |[Federated Learning over Wireless Networks: Optimization Model Design and Analysis](https://ieeexplore.ieee.org/document/8737464)|[OnDevAI](https://github.com/nhatminh/OnDevAI)| 199 | |[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-)| 200 | [A joint learning and communications framework for federated learning over wireless networks](https://arxiv.org/pdf/1909.07972.pdf)|[Wireless-FL](https://github.com/mzchen0/Wireless-FL)| 201 | 202 | ### Multiple access scheduling and routing using machine learning techniques 203 | | Paper | Code | 204 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 205 | |[Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach](https://ieeexplore.ieee.org/document/8474348)|[DQN_RC_DSA_IOT2019](https://github.com/haohsuan2918/DQN_RC_DSA_IOT2019)| 206 | |[Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://ieeexplore.ieee.org/document/8303773)|[DynamicMultiChannelRL](https://github.com/GulatiAditya/DynamicMultiChannelRL)| 207 | | [Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks](https://ieeexplore.ieee.org/document/8254101)| [shkrwnd](https://github.com/shkrwnd)/[Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access](https://github.com/shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access) | 208 | |[Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://ieeexplore.ieee.org/document/8303773)|[DynamicMultiChannelRL](https://github.com/GulatiAditya/DynamicMultiChannelRL)| 209 | |[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)| 210 | |[Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning](https://arxiv.org/abs/2102.07019)|[FLDRL-in-Wireless-Communication](https://github.com/Mauriyin/FLDRL-in-Wireless-Communication)| 211 | |[A Clustering Approach to Wireless Scheduling](https://ieeexplore.ieee.org/abstract/document/9154271)|[A_Clustering_Approach_to_Wireless_Scheduling](https://github.com/willtop/A_Clustering_Approach_to_Wireless_Scheduling)| 212 | |[Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://ieeexplore.ieee.org/document/8665952)|[DLMA](https://github.com/YidingYu/DLMA)| 213 | | [A deep-reinforcement learning approach for software-defined networking routing optimization](https://arxiv.org/abs/1709.07080) | [knowledgedefinednetworking / a-deep-rl-approach-for-sdn-routing-optimization](https://github.com/knowledgedefinednetworking/a-deep-rl-approach-for-sdn-routing-optimization) | 214 | | [Spatial deep learning for wireless scheduling](https://arxiv.org/abs/1808.01486) | [willtop](https://github.com/willtop)/[Spatial_DeepLearning_Wireless_Scheduling](https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling) | 215 | | [Transformer based Online Bayesian Neural Networks for Grant Free Uplink Access in CRAN with Streaming Variational Inference](https://ieeexplore.ieee.org/document/9540910) | [CRAN_MIMO_VI](https://github.com/jhanilesh96/CRAN_MIMO_VI) | 216 | 217 | ### Machine learning for software-defined networking 218 | | Paper | Code | 219 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 220 | | [DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls](https://link.springer.com/chapter/10.1007/978-3-030-19945-6_10)| [ruihuili / DELMU](https://github.com/ruihuili/DELMU) | 221 | |[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)| 222 | 223 | ### Machine learning for emerging communication systems and applications 224 | | Paper | Code | 225 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 226 | |[Deep Reinforcement Learning with Communication Transformer for Adaptive Live Streaming in Wireless Edge Networks](https://ieeexplore.ieee.org/document/9605672)|[SACCT](https://github.com/wsyCUHK/SACCT)| 227 | |[Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/9627763)|[RLTaskOffloading](https://github.com/linkpark/RLTaskOffloading)| 228 | |[Fast Adaptive Computation Offloading in Edge Computing based on Meta Reinforcement Learning](https://arxiv.org/abs/2008.02033)|[metarl-offloading](https://github.com/linkpark/metarl-offloading)| 229 | |[Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks](https://ieeexplore.ieee.org/document/9449944)|[LyDROO](https://github.com/revenol/LyDROO)| 230 | |[Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach](https://arxiv.org/abs/2103.03817)|[ZT-PFR](https://github.com/wildsky95/ZT-PFR)| 231 | |[Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://arxiv.org/abs/2010.12461)|[uav_data_harvesting](https://github.com/hbayerlein/uav_data_harvesting)| 232 | |[Spectrum sharing in vehicular networks based on multi-agent reinforcement learning](https://arxiv.org/abs/1905.02910)|[MARLspectrumSharingV2X](https://github.com/AlexVic/MARLspectrumSharingV2X)| 233 | |[An Open-Source Framework for Adaptive Traffic Signal Control](https://arxiv.org/pdf/1909.00395.pdf)|[docwza/sumolights](https://github.com/docwza/sumolights)| 234 | |[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)| 235 | |[BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems](https://ieeexplore.ieee.org/abstract/document/9145068)|[BottleNetPlusPlus](https://github.com/shaojiawei07/BottleNetPlusPlus)| 236 | |[Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks](https://ieeexplore.ieee.org/abstract/document/8771176/)|[DROO](https://github.com/revenol/DROO)| 237 | |[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)| 238 | |[Graph Neural Network for Large-Scale Network Localization](https://arxiv.org/abs/2010.11653)|[GNN-For-localization](https://github.com/Yanzongzi/GNN-For-localization)| 239 | |[Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning](https://arxiv.org/abs/2008.02033)|[metarl-offloading](https://github.com/linkpark/metarl-offloading)| 240 | |[RF-based Direction Finding of UAVs Using DNN](https://arxiv.org/abs/1712.01154)|https://github.com/LahiruJayasinghe/DeepDOA| 241 | ### Secure machine learning over communication networks 242 | | Paper | Code | 243 | | ------------------------------------------------------------ | ------------------------------------------------------------ | 244 | | [Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems](https://arxiv.org/abs/1902.08391)| https://github.com/meysamsadeghi/Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems | 245 | |[Deep Learning for the Gaussian Wiretap Channel](https://ieeexplore.ieee.org/abstract/document/8761681)|[NN_GWTC](https://github.com/Fritschek/NN_GWTC)| 246 | 247 | 248 | 249 | # "通信+DL"论文（无代码)/Paper List Without Code 250 | 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) 251 | * [Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach](https://arxiv.org/pdf/1903.12546.pdf) 252 | * [Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1904.00601.pdf) 253 | * [Machine Learning for Wireless Communication Channel Modeling: An Overview](https://link.springer.com/article/10.1007/s11277-019-06275-4) 254 | * [Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning](https://arxiv.org/pdf/1903.08163.pdf) 255 | 256 | # 数据集/Database 257 | * [Wireless-Signal-Strength-on-2.4GHz-WSS24-dataset](https://github.com/postman511/Wireless-Signal-Strength-on-2.4GHz-WSS24-dataset):A Dataset For RSSI Analysis 258 | * [MetaCC](https://github.com/ruihuili/MetaCC):[A Channel Coding Benchmark for Meta-Learning](https://openreview.net/forum?id=DjzPaX8AT0z) 259 | * [thymio-radio-map](https://github.com/arthurgassner/thymio-radio-map): [OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting](https://arxiv.org/abs/2104.07963) 260 | * [The DeepMIMO Dataset](http://deepmimo.net/) and the corresponding paper [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://arxiv.org/abs/1902.06435) 261 | * [RAYMOBTIME](https://www.lasse.ufpa.br/raymobtime/):Raymobtime is a methodology for collecting realistic datasets for simulating wireless communications. It uses ray-tracing and 3D scenarios with mobility and time evolution, for obtaining consistency over time, frequency and space. 262 | * [MASSIVE MIMO CSI MEASUREMENTS](https://homes.esat.kuleuven.be/~sdebast/csi_measurements.html) 263 | * [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) 264 | * [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)。 265 | > To the best of our knowledge,this is the first open dataset of real modulated signals 266 | > for wireless communication systems. 267 | * [RF DATASETS FOR MACHINE LEARNING](https://www.deepsig.io/datasets) 268 | * [open datase](https://pan.baidu.com/s/1rS143bEDaOTEiCneXE67dg#list/path=%2F):来自论文[Signal Demodulation With Machine Learning 269 | Methods for Physical Layer Visible Light 270 | Communications: Prototype Platform, 271 | Open Dataset, and Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8661606&tag=1) 272 | >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. 273 | # 学者个人主页/Researcher Homepage 274 | * [Dr. Zhen Gao ( 高 镇 )](https://gaozhen16.eu.org/): 275 | - Wireless Communications 276 | - Channel Estimation of mmWave/THz Hybrid Massive MIMO 277 | - Sparse Signal Processing 278 | - Deep Learning based Solutions in Wireless Systems 279 | * [Ahmed Alkhateeb](http://www.aalkhateeb.net/index.html):Research Interests 280 | - Millimeter Wave and Massive MIMO Communication 281 | - Vehicular and Drone Communication Systems 282 | - Applications of Machine Learning in Wireless Communication 283 | - Building Mobile Communication Systems that Work in Reality! 284 | * [Emil Björnson](https://ebjornson.com/research/): 285 | He performs research on multi-antenna communications, Massive MIMO, radio resource allocation, energy-efficient communications, and network design. 286 | * [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. 287 | # 有用的网页和材料/Useful Websites and Materials 288 | * [Graph-based Deep Learning for Communication Networks: A Survey](https://arxiv.org/abs/2106.02533): [GNN-Communication-Networks](https://github.com/jwwthu/GNN-Communication-Networks) 289 | * [机器学习和通信结合论文列表/Research Library ](https://mlc.committees.comsoc.org/research-library/) 290 | * [Best Readings in Machine Learning in Communications](https://www.comsoc.org/publications/best-readings/machine-learning-communications) 291 | * [Communication Systems, Linköping University, LIU](https://www.youtube.com/channel/UCOrjRoYJPqGiR1SZvU3xcYQ/videos) 292 | * [Codes for Intelligent reflecting surface (IRS)](https://github.com/ken0225/RIS_Codes_Collection) 293 | * [awesome-ml4co](https://github.com/Thinklab-SJTU/awesome-ml4co):a list of papers that utilize machine learning technologies to solve combinatorial optimization problems. 294 | * [Simulation Code from comsoc](https://mlc.committees.comsoc.org/papers-with-code/) 295 | 296 |
贡献者/Contributors： 297 | * WxZhu: 298 | - [Github](https://github.com/zhuwenxing) 299 | - Email:wenxingzhu@shu.edu.cn 300 | * [LinTian](https://github.com/TianLin0509) 301 | * [HongtaiChen](https://github.com/HongtaiChen) 302 | * [yihanjiang](https://github.com/yihanjiang) 303 | * wu huaming: 304 | - Email:whming@tju.edu.cn 305 | 306 |
版本更新/Version Update： 307 | 308 | 1. 第一版完成/First Version：2019-02-21 309 | 2. 分类整理及链接补全/First Version: 2021-04-14 via [Yokoxue](https://github.com/yokoxue) 310 | --------------------------------------------------------------------------------