For English reader,please refer to English Version.

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。
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。
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^_^
温馨提示:watch相较于star更容易得到更新通知 。
TODO

• 按不同小方向分类
• 论文添加下载链接
• 增加更多相关论文代码
  • 在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
  • 该Repo通过爬虫+Github Action实现每日自动更新
• 传统通信论文代码列表
• “通信+DL”论文列表（引用较高，可以没有代码）

目录 （Contents)

• Topics
  • Machine/deep learning for physical layer optimization
  • Resource, power and network optimization using machine learning techniques
  • Distributed learning algorithms over communication networks
  • Multiple access scheduling and routing using machine learning techniques
  • Machine learning for network slicing, network virtualization, and software-defined networking
  • Machine learning for emerging communication systems and applications (e.g., IoT, edge computing, caching, smart cities, vehicular networks, and localization)
  • Secure machine learning over communication networks

Topics

Physical layer optimization

Paper	Code
Online Meta-Learning For Hybrid Model-Based Deep Receivers	meta-deepsic
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access	jadce
sionna: an open-source library for next-generation physical layer research	sionna
Deep Learning Aided Robust Joint Channel Classification, Channel Estimation, and Signal Detection for Underwater Optical Communication	UWOC-JCCESD
LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers	LoRD-Net
Deep Diffusion Models for Robust Channel Estimation	diffusion-channels
A Channel Coding Benchmark for Meta-Learning	MetaCC
On the Feasibility of Modeling OFDM Communication Signals with Unsupervised Generative Adversarial Networks	OFDM-GAN
Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals	LearningML
iterative error decimation for syndrome-based neural network decoders	ied
ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning	kocodes
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications	CDRN-channel-estimation-IRS
Model-Driven Deep Learning for MIMO Detection	OAMP-Net
Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems	DCRNet
Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming	HBF-Net
CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback	CLNet
Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation	B_DNN
Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment	DL-ActiveLearning-BeamAlignment
Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO	Source-Code-X.Ma
Deep Learning Predictive Band Switching in Wireless Networks	Bandswitch-DeepMIMO
RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection	RE-MIMO
NOLD: A Neural-Network Optimized Low-Resolution Decoder for LDPC Codes	NOLD
A MIMO detector with deep learning in the presence of correlated interference	project_dcnnmld
Deep Learning Driven Non-Orthogonal Precoding for Millimeter Wave Communications	Deep-Learning-Driven-Non-Orthogonal-Precoding-for-Millimeter-Wave-Communications
Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems	DeepUnfolding_WMMSE
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems	haoyye/OFDM_DNN
Automatic Modulation Classification: A Deep Learning Enabled Approach	mengxiaomao/CNN_AMC
Deep Architectures for Modulation Recognition	qieaaa / Deep-Architectures-for-Modulation-Recognition
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networkss	zhongyuanzhao / dl_ofdm
Joint Transceiver Optimization for Wireless Communication PHY with Convolutional NeuralNetwork	hlz1992/RadioCNN
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning	lasseufpa/5gm-data
A Two-Fold Group Lasso Based Lightweight Deep Neural Network for Automatic Modulation Classification	Group-Sparse-DNN-for-AMC
Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification	MultiStage-Grassmannian-DNN
Deep Learning for Massive MIMO CSI Feedback	sydney222 / Python_CsiNet
Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning	TianLin0509/BF-design-with-DL
An Introduction to Deep Learning for the Physical Layer	yashcao / RTN-DL-for-physical-layer musicbeer / Deep-Learning-for-the-Physical-Layer helloMRDJ / autoencoder-for-the-Physical-Layer
Deep MIMO Detection	neevsamuel/DeepMIMODetection
Learning to Detect	neevsamuel/LearningToDetect
An iterative BP-CNN architecture for channel decoding	liangfei-info/Iterative-BP-CNN
On Deep Learning-Based Channel Decoding	gruberto/DL-ChannelDecoding Decoder-using-deep-learning
Deep learning-based channel estimation for beamspace mmWave massive MIMO systems	hehengtao/LDAMP_based-Channel-estimation
Fast Deep Learning for Automatic Modulation Classification	dl4amc/source
Deep Learning-Based Channel Estimation	Mehran-Soltani/ChannelNet
Sparsely Connected Neural Network for Massive MIMO Detection	MIMO_Detection
Deepcode: Feedback Codes via Deep Learning	https://github.com/hyejikim1/Deepcode https://github.com/yihanjiang/feedback_code
MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders	MIST_CNN_Decoder
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors	modulation_classif
Learning Physical-Layer Communication with Quantized Feedback	quantizedfeedback
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding	RLdecoding
Adaptive Neural Signal Detection for Massive MIMO	mehrdadkhani/MMNet
CNN-based Precoder and Combiner Design in mmWave MIMO Systems	Deep_HybridBeamforming
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification	coming soon
Low-Precision Neural Network Decoding of Polar Codes	low-precision-nnd
Low-rank mmWave MIMO channel estimation in one-bit receivers	Low-rank-MIMO-channel-estimation-from-one-bit-measurements
Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots	1-Bit-ADCs
Deep Learning for Direct Hybrid Precoding in Millimeter Wave Massive MIMO Systems	DL-hybrid-precoder
Deep Learning-Based Detector for OFDM-IM	DeepIM
Deep Learning for Channel Coding via Neural Mutual Information Estimation	Wireless_encoding_with_MI_estimation
Learning the MMSE Channel Estimator	learning-mmse-est
Neural Network Aided SC Decoder for Polar Codes	1_NND
Exploiting Bi-Directional Channel Reciprocity in Deep Learning for Low Rate Massive MIMO CSI Feedback	Bi-Directional-Channel-Reciprocity
Performance Evaluation of Channel Decoding With Deep Neural Networks	deep-neural-network-decoder
Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design	Genetic-Algorithm-based-LDPC-Code-Design
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication	DeepLearning_MIMO
Learned Conjugate Gradient Descent Network for Massive MIMO Detection	LcgNet
Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach	overloaded_MIMO
Deep Soft Interference Cancellation for MIMO Detection	DeepSIC
Deep unfolding of the weighted MMSE algorithm	WMMSE-deep-unfolding
Deep Learning for Direction of Arrival Estimation via Emulation of Large Antenna Arrays	DoA with DNN via Emulation of Antenna Arrays
Acquiring Measurement Matrices via Deep Basis Persuit for Sparse Channel Estimation in mmWave Massive MIMO Systems	DeepBP-AE
Deep Learning for SVD and Hybrid Beamforming	DL_SVD_BF
Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison	Reverse-Jensen_MI_estimation
Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems	Codes-for-Deep-Transfer-Learning-Based-Downlink-Channel-Prediction-for-FDD-Massive-MIMO-Systems
Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN	Channel_Estimation_cGAN
A Model-Driven Deep Learning Method for Normalized Min-Sum LDPC Decoding	A-Model-Driven-Deep-Learning-Method-for-Normalized-Min-Sum-LDPC-Decoding
Complex-Valued Convolutions for Modulation Recognition using Deep Learning	Complex_Convolutions
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems	GAN-cov-matrix
Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems	Simulation Codes
Deep Learning for Polar Codes over Flat Fading Channels	polarOverFlatFading
Aggregated Network for Massive MIMO CSI Feedback	ACRNet
Convolutional Radio Modulation Recognition Networks	chrisruk/cnn qieaaa / Singal-CNN
Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels	yihanjiang/turboae
Multi-resolution CSI Feedback with deep learning in Massive MIMO System	CRNet
Spatio-Temporal Representation with Deep Recurrent Network in MIMO CSI Feedback	ConvlstmCsiNet
Learn to Compress CSI and Allocate Resources in Vehicular Networks	Learn-CompressCSI-RA-V2X-Code
Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency	DL-Massive-MIMO
Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink	UL2DL
Towards Optimally Efficient Tree Search with Deep Temporal Difference Learning	hats
Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning	LIS-DeepLearning
A CNN-Based End-to-End Learning Framework Towards Intelligent Communication Systems	Deepcom
Communication Algorithms via Deep Learning	yihanjiang/commviadl
Learning to Communicate in a Noisy Environment	echo
Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels	meta-autoencoder
Deep energy autoencoder for noncoherent multicarrier MU-SIMO systems	energy_autoencoder
Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems	deepChannelLearning4RIS
Deep learning based end-to-end wireless communication systems with conditional GAN as unknown channel	End2End_GAN
RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks	RadioUNet
Deep learning aided multicarrier systems	multicarrier_autoencoder

Resource and network optimization

Paper	Code
Resource Allocation based on Graph Neural Networks in Vehicular Communications	Globecom2020-ResourceAllocationGNN
An Unsupervised Deep Unrolling Framework for Constrained Optimization Problems in Wireless Networks	USRMNet-HWGCN
Power Allocation for Wireless Federated Learning using Graph Neural Networks	WirelessFL-PDGNet
Delay-Oriented Distributed Scheduling Using Graph Neural Networks	gcn-dql
Deep Learning Based MAC via Joint Channel Access and Rate Adaptation	Wireless-Signal-Strength-on-2.4GHz-WSS24-dataset
wireless link scheduling via graph representation learning: a comparative study of different supervision levels	LinkSchedulingGNNs_SupervisionStudy
Distributed Scheduling using Graph Neural Networks	distgcn
DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks	deepbeam
Graph Embedding-Based Wireless Link Scheduling With Few Training Samples	graph_embedding_link_scheduling
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks	mkoz71 / Energy-Efficiency-in-Reinforcement-Learning
Learning to optimize: Training deep neural networks for wireless resource management	Haoran-S / DNN_WMMSE
Implications of Decentralized Q-learning Resource Allocation in Wireless Networks	wn-upf / decentralized_qlearning_resource_allocation_in_wns
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement	farismismar / Deep-Q-Learning-SON-Perf-Improvement
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells	farismismar / Q-Learning-Power-Control
Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks	bmatthiesen / deep-EE-opt
Actor-Critic-Based Resource Allocation for Multi-modal Optical Networks	BoyuanYan / Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks
Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication	seotaijiya/TPC_D2D
Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learning	qfnet
Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks	DL-CoMP-Machine-Learning
Deep Reinforcement Learning for Resource Allocation in V2V Communications	https://github.com/haoyye/ResourceAllocationReinforcementLearning
AIF: An Artificial Intelligence Framework for Smart Wireless Network Management	caogang/WlanDqn
Deep-Learning-Power-Allocation-in-Massive-MIMO	lucasanguinetti / Deep-Learning-Power-Allocation-in-Massive-MIMO
Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks	DPPL
Learning Based Power Control for mmWave Massive MIMO against Jamming	Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming
Towards Optimal Power Control via Ensembling Deep Neural Networks	PCNet-ePCNet
A Graph Neural Network Approach for Scalable Wireless Power Control	Globecom2019
Mobility-Aware Centralized Reinforcement Learning for Dynamic Resource Allocation in HetNets	UARA
Intelligent Resource Allocation in Wireless Communications Systems	IRAWCS
Learning Combinatorial Optimization Algorithms over Graphs	graph_comb_opt
Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management	RNNASIP
Power Allocation in Multi-user Cellular Networks With Deep Q Learning Approach	PA_ICC
Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches	PA_TWC
Unfolding WMMSE using Graph Neural Networks for Efficient Power Allocation	Unrolled-WMMSE
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks	Power-Control-asilomar
Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis	GNN-Resource-Management
Contrastive Self-Supervised Learning for Wireless Power Control	ContrastiveSSL_WirelessPowerControl
No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks	CRNOMA_DDPG
Multicell Power Control under Rate Constraints with Deep Learning	SRnet-and-SRNet-Heu-for-power-control
Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels	Sub6-Preds-mmWave
Wireless link adaptation - a hybrid data-driven and model-based approach	LinkAdaptationCSI
Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment	ICASSP2021
DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning	zaxliu/deepnap
No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks	CRNOMA_DDPG

Distributed learning algorithms over communication networks

Paper	Code
A Scalable Federated Multi-agent Architecture for Networked Connected Communication Network	Fed-MF-MAL
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach	RIS-FL
Decentralized Statistical Inference with Unrolled Graph Neural Networks	Learning-based-DOP-Framework
Decentralized Scheduling for Cooperative Localization with Deep Reinforcement Learning	DeepRLVehicularLocalization
Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination	DRL_for_DDBC
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach	swordest/mec_drl
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation	FEDL
Federated Learning over Wireless Networks: Optimization Model Design and Analysis	OnDevAI
Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications	Energy-Harvesting-DDPG
A joint learning and communications framework for federated learning over wireless networks	Wireless-FL

Multiple access scheduling and routing using machine learning techniques

Paper	Code
Distributive Dynamic Spectrum Access Through Deep Reinforcement Learning: A Reservoir Computing-Based Approach	DQN_RC_DSA_IOT2019
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks	DynamicMultiChannelRL
Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks	shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks	DynamicMultiChannelRL
Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks	AoI_RL
Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning	FLDRL-in-Wireless-Communication
A Clustering Approach to Wireless Scheduling	A_Clustering_Approach_to_Wireless_Scheduling
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks	DLMA
A deep-reinforcement learning approach for software-defined networking routing optimization	knowledgedefinednetworking / a-deep-rl-approach-for-sdn-routing-optimization
Spatial deep learning for wireless scheduling	willtop/Spatial_DeepLearning_Wireless_Scheduling
Transformer based Online Bayesian Neural Networks for Grant Free Uplink Access in CRAN with Streaming Variational Inference	CRAN_MIMO_VI

Machine learning for software-defined networking

Paper	Code
DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls	ruihuili / DELMU
ns-3 meets OpenAI Gym: The Playground for Machine Learning in Networking Research	ns3-gym

Machine learning for emerging communication systems and applications

Paper	Code
Deep Reinforcement Learning with Communication Transformer for Adaptive Live Streaming in Wireless Edge Networks	SACCT
Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning	RLTaskOffloading
Fast Adaptive Computation Offloading in Edge Computing based on Meta Reinforcement Learning	metarl-offloading
Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks	LyDROO
Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach	ZT-PFR
Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning	uav_data_harvesting
Spectrum sharing in vehicular networks based on multi-agent reinforcement learning	MARLspectrumSharingV2X
An Open-Source Framework for Adaptive Traffic Signal Control	docwza/sumolights
CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks	MaMIMO_CSI_with_CNN_positioning
BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems	BottleNetPlusPlus
Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks	DROO
MaMIMO CSI-based positioning using CNNs: Peeking inside the black box	inside-the-black-box
Graph Neural Network for Large-Scale Network Localization	GNN-For-localization
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning	metarl-offloading
RF-based Direction Finding of UAVs Using DNN	https://github.com/LahiruJayasinghe/DeepDOA

Secure machine learning over communication networks

Paper	Code
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems	https://github.com/meysamsadeghi/Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems
Deep Learning for the Gaussian Wiretap Channel	NN_GWTC

"通信+DL"论文（无代码)/Paper List Without Code

说明：论文主要来源于arxiv中Signal Processing和Information Theory

• Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
• Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach
• Machine Learning for Wireless Communication Channel Modeling: An Overview
• Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning

数据集/Database

• Wireless-Signal-Strength-on-2.4GHz-WSS24-dataset:A Dataset For RSSI Analysis
• MetaCC:A Channel Coding Benchmark for Meta-Learning
• thymio-radio-map: OpenCSI: An Open-Source Dataset for Indoor Localization Using CSI-Based Fingerprinting
• The DeepMIMO Dataset and the corresponding paper DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications
• 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.
• MASSIVE MIMO CSI MEASUREMENTS
• SM-CsiNet+ and PM-CsiNet+:来自论文Convolutional Neural Network based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis
• An open online real modulated dataset:来自论文Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics。

  To the best of our knowledge,this is the first open dataset of real modulated signals for wireless communication systems.

• RF DATASETS FOR MACHINE LEARNING
• open datase:来自论文Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms

  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.

学者个人主页/Researcher Homepage

• Dr. Zhen Gao ( 高 镇 ):
  • Wireless Communications
  • Channel Estimation of mmWave/THz Hybrid Massive MIMO
  • Sparse Signal Processing
  • Deep Learning based Solutions in Wireless Systems
• Ahmed Alkhateeb:Research Interests
  • Millimeter Wave and Massive MIMO Communication
  • Vehicular and Drone Communication Systems
  • Applications of Machine Learning in Wireless Communication
  • Building Mobile Communication Systems that Work in Reality!
• Emil Björnson: He performs research on multi-antenna communications, Massive MIMO, radio resource allocation, energy-efficient communications, and network design.
• 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.

有用的网页和材料/Useful Websites and Materials

• Graph-based Deep Learning for Communication Networks: A Survey: GNN-Communication-Networks
• 机器学习和通信结合论文列表/Research Library
• Best Readings in Machine Learning in Communications
• Communication Systems, Linköping University, LIU
• Codes for Intelligent reflecting surface (IRS)
• awesome-ml4co:a list of papers that utilize machine learning technologies to solve combinatorial optimization problems.
• Simulation Code from comsoc

贡献者/Contributors：

• WxZhu:
  • Github
  • Email:wenxingzhu@shu.edu.cn
• LinTian
• HongtaiChen
• yihanjiang
• wu huaming:
  • Email:whming@tju.edu.cn

版本更新/Version Update：

1. 第一版完成/First Version：2019-02-21
2. 分类整理及链接补全/First Version: 2021-04-14 via Yokoxue