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

无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

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）

代码复现与交流群

交流群

目录 （Contents)

Topics

Physical layer optimization

Paper Code
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
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

数据集/Database

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


贡献者/Contributors：


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