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

**Repository Path**: liuyi33479/Paper-with-Code-of-Wireless-communication-Based-on-DL

## Basic Information

- **Project Name**: Paper-with-Code-of-Wireless-communication-Based-on-DL
- **Description**: 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
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-11-19
- **Last Updated**: 2025-05-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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).

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。
<br>
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。
<br>
个人关注的领域和精力有限，这个列表不会那么完整。如果你知道一些相关的开源论文，但不在此列表中，非常欢迎添加，为community贡献一份力量。欢迎交流^_^
<br>
**温馨提示:watch相较于star更容易得到更新通知 。**
<br>
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
- [ ] 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）



# 论文/Paper


| Paper                                                        | Code                                                         |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
|[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)|
|[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)|
|[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)|
|[Learning Combinatorial Optimization Algorithms over Graphs](https://arxiv.org/abs/1704.01665)|[graph_comb_opt](https://github.com/Hanjun-Dai/graph_comb_opt.git)|
|[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)|
|[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)|
|[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)|
|[Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://arxiv.org/pdf/1910.13067.pdf)|[FEDL](https://github.com/nhatminh/FEDL)|
|[Federated Learning over Wireless Networks: Optimization Model Design and Analysis](https://ieeexplore.ieee.org/document/8737464)|[OnDevAI](https://github.com/nhatminh/OnDevAI)|
|[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)|
|[Intelligent Resource Allocation in Wireless Communications Systems](https://ieeexplore.ieee.org/document/8961912)|[IRAWCS](https://github.com/seotaijiya/IRAWCS)|
|[Spatio-Temporal Representation with Deep Recurrent Network in MIMO CSI Feedback](https://ieeexplore.ieee.org/document/8951228)|[ConvlstmCsiNet](https://github.com/Aries-LXY/ConvlstmCsiNet)|
|[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)|
|[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)|
|[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)|
|[Learning the MMSE Channel Estimator](https://arxiv.org/pdf/1707.05674v3.pdf)|[learning-mmse-est](https://github.com/tum-msv/learning-mmse-est)|
|[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-)|
|[Model-Aware Deep Architectures for One-Bit Compressive Variational Autoencoding](https://arxiv.org/abs/1911.12410)|[deep1bitVAE](https://github.com/skhobahi/deep1bitVAE) *Not Yet*|
|[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)|
|[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)|
|[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)|
|[Deep Learning for the Gaussian Wiretap Channel](https://ieeexplore.ieee.org/abstract/document/8761681)|[NN_GWTC](https://github.com/Fritschek/NN_GWTC)|
|[Multi-resolution CSI Feedback with deep learning in Massive MIMO System](https://arxiv.org/abs/1910.14322)|[CRNet](https://github.com/Kylin9511/CRNet) *Recommend! very detailed README* |
|[Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://ieeexplore.ieee.org/document/8665952)|[DLMA](https://github.com/YidingYu/DLMA)|
|[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)|
|[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)|
|[Deep Learning-Based Detector for OFDM-IM](https://ieeexplore.ieee.org/document/8684894)|[DeepIM](https://github.com/ThienVanLuong/DeepIM)|
|[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)|
|[Learning to Communicate in a Noisy Environment](https://arxiv.org/abs/1910.09630)|[echo](https://github.com/ml4wireless/echo)|
|[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)|
|[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)|
|[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)|
| Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels  | [yihanjiang](https://github.com/yihanjiang)/[turboae](https://github.com/yihanjiang/turboae) |
| Communication Algorithms via Deep Learning  | [yihanjiang](https://github.com/yihanjiang)/[commviadl](https://github.com/yihanjiang/Sequential-RNN-Decoder) |
|[Towards Optimal Power Control via Ensembling Deep Neural Networks](https://arxiv.org/abs/1807.10025)|[PCNet-ePCNet](https://github.com/ShenGroup/PCNet-ePCNet)|
|[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)|
|[A Graph Neural Network Approach for Scalable Wireless Power Control](https://arxiv.org/pdf/1907.08487.pdf)|[Globecom2019](https://github.com/yshenaw/Globecom2019)|
|[CNN-based Precoder and Combiner Design in mmWave MIMO Systems](https://ieeexplore.ieee.org/document/8710287)|[Deep_HybridBeamforming](https://github.com/meuseabe/Deep_HybridBeamforming)|
|[Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification](https://arxiv.org/pdf/1909.03050.pdf)|[coming soon](https://github.com/kython)|
|[An Open-Source Framework for Adaptive Traffic Signal Control](https://arxiv.org/pdf/1909.00395.pdf)|[docwza/sumolights](https://github.com/docwza/sumolights)|
|[A CNN-Based End-to-End Learning Framework Towards Intelligent Communication Systems](https://ieeexplore.ieee.org/document/8755977)|[Deepcom](https://github.com/ZhangKaiyao/Deepcom)|
|[Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding](https://arxiv.org/pdf/1906.04448.pdf)|[RLdecoding](https://github.com/fabriziocarpi/RLdecoding)|
|[Adaptive Neural Signal Detection for Massive MIMO](https://arxiv.org/abs/1906.04610)|[mehrdadkhani/MMNet](https://github.com/mehrdadkhani/MMNet)|
|[Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks](https://ieeexplore.ieee.org/document/8303773)|[DynamicMultiChannelRL](https://github.com/GulatiAditya/DynamicMultiChannelRL)|
| Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells|[Q-Learning-Power-Control](https://github.com/farismismar/Q-Learning-Power-Control)|
|[Spectrum sharing in vehicular networks based on multi-agent reinforcement learning](https://arxiv.org/abs/1905.02910)|[MARLspectrumSharingV2X](https://github.com/AlexVic/MARLspectrumSharingV2X)|
|[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)|
|Learning Physical-Layer Communication with Quantized Feedback|[quantizedfeedback](https://github.com/henkwymeersch/quantizedfeedback)|
|[Decentralized Scheduling for Cooperative Localization with Deep Reinforcement Learning](https://ieeexplore.ieee.org/abstract/document/8701533)|[DeepRLVehicularLocalization](https://github.com/henkwymeersch/DeepRLVehicularLocalization)|
|Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks|[DynamicMultiChannelRL](https://github.com/GulatiAditya/DynamicMultiChannelRL)|
|MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders|[MIST_CNN_Decoder](https://github.com/kryashashwi/MIST_CNN_Decoder)|
|[Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink](https://arxiv.org/abs/1812.07518)|[UL2DL](https://github.com/safarisadegh/UL2DL)|
|Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency|[DL-Massive-MIMO](https://github.com/malrabeiah/DL-Massive-MIMO)|
|Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks|[DPPL](https://github.com/stochastic-geometry/DPPL)|
|Learning Based Power Control for mmWave Massive MIMO against Jamming|[Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming](https://github.com/xiaozhch5/Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming)|
|Sparsely Connected Neural Network for Massive MIMO Detection|[MIMO_Detection](https://github.com/NobleLee/MIMO_Detection)|
|[Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learningg](https://ieeexplore.ieee.org/abstract/document/8690757)|[qfnet](https://github.com/kangcp/qfnet)|
|Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks|[DL-CoMP-Machine-Learning](https://github.com/farismismar/DL-CoMP-Machine-Learning)|
|Deep Reinforcement Learning for Resource Allocation in V2V Communications|https://github.com/haoyye/ResourceAllocationReinforcementLearning|
|RF-based Direction Finding of UAVs Using DNN|https://github.com/LahiruJayasinghe/DeepDOA|
| Deepcode: Feedback Codes via Deep Learning                   | https://github.com/hyejikim1/Deepcode<br>https://github.com/yihanjiang/feedback_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 |
| [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) |
| Deep-Learning-Power-Allocation-in-Massive-MIMO               | [lucasanguinetti / Deep-Learning-Power-Allocation-in-Massive-MIMO](https://github.com/lucasanguinetti/Deep-Learning-Power-Allocation-in-Massive-MIMO) |
| DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications | [The DeepMIMO Dataset](http://deepmimo.net/)                 |
| Fast Deep Learning for Automatic Modulation Classification   | [dl4amc](https://github.com/dl4amc)/[source](https://github.com/dl4amc/source) |
| Deep Learning-Based Channel Estimation                       | [Mehran-Soltani](https://github.com/Mehran-Soltani)/[ChannelNet](https://github.com/Mehran-Soltani/ChannelNet) |
| 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) |
| 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) |
| Spatial deep learning for wireless scheduling                | [willtop](https://github.com/willtop)/[Spatial_DeepLearning_Wireless_Scheduling](https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling) |
| 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) |
| 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) |
| 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) |
| Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks | [bmatthiesen / deep-EE-opt](https://github.com/bmatthiesen/deep-EE-opt) |
| 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) |
| Deep MIMO Detection                                          | [neevsamuel](https://github.com/neevsamuel)/[DeepMIMODetection](https://github.com/neevsamuel/DeepMIMODetection) |
| Learning to Detect                                           | [neevsamuel](https://github.com/neevsamuel)/[LearningToDetect](https://github.com/neevsamuel/LearningToDetect) |
| 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) |
| On Deep Learning-Based Channel Decoding                      | [gruberto/DL-ChannelDecoding](https://github.com/gruberto/DL-ChannelDecoding) <br/>[Decoder-using-deep-learning](https://github.com/VivekRamalingamK/Decoder-using-deep-learning)|
| DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls | [ruihuili / DELMU](https://github.com/ruihuili/DELMU)        |
| 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) |
| An Introduction to Deep Learning for the Physical Layer      | [yashcao / RTN-DL-for-physical-layer](https://github.com/yashcao/RTN-DL-for-physical-layer)<br />[musicbeer / Deep-Learning-for-the-Physical-Layer](https://github.com/musicbeer/Deep-Learning-for-the-Physical-Layer)<br />[helloMRDJ / autoencoder-for-the-Physical-Layer](https://github.com/helloMRDJ/autoencoder-for-the-Physical-Layer) |
| Convolutional Radio Modulation Recognition Networks          | [chrisruk](https://github.com/chrisruk)/[cnn](https://github.com/chrisruk/cnn)<br />[qieaaa / Singal-CNN](https://github.com/qieaaa/Singal-CNN) |
| Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks | [zhongyuanzhao / dl_ofdm](https://github.com/zhongyuanzhao/dl_ofdm) |
| Joint Transceiver Optimization for WirelessCommunication PHY with Convolutional NeuralNetwork | [hlz1992/RadioCNN](https://github.com/hlz1992/RadioCNN)      |
| Deep Learning for Massive MIMO CSI Feedback                  | [sydney222 / Python_CsiNet](https://github.com/sydney222/Python_CsiNet) |
| [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)|
| 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) |
| 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) |
| DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning | [zaxliu](https://github.com/zaxliu)/[deepnap](https://github.com/zaxliu/deepnap) |
| Automatic Modulation Classification: A Deep Learning Enabled Approach | [mengxiaomao](https://github.com/mengxiaomao)/[CNN_AMC](https://github.com/mengxiaomao/CNN_AMC) |
| Deep Architectures for Modulation Recognition                | [qieaaa / Deep-Architectures-for-Modulation-Recognition](https://github.com/qieaaa/Deep-Architectures-for-Modulation-Recognition) |
| Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks | [mkoz71 / Energy-Efficiency-in-Reinforcement-Learning](https://github.com/mkoz71/Energy-Efficiency-in-Reinforcement-Learning) |
| Learning to optimize: Training deep neural networks for wireless resource management | [Haoran-S / DNN_WMMSE](https://github.com/Haoran-S/DNN_WMMSE) |
| 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) |
| Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems | [haoyye/OFDM_DNN](https://github.com/haoyye/OFDM_DNN)        |

# "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](https://arxiv.org/list/eess.SP/recent)和[Information Theory](https://arxiv.org/list/cs.IT/recent)
* [Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach](https://arxiv.org/pdf/1903.12546.pdf)
* [Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1904.00601.pdf)
* [Machine Learning for Wireless Communication Channel Modeling: An Overview](https://link.springer.com/article/10.1007/s11277-019-06275-4)
* [Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning](https://arxiv.org/pdf/1903.08163.pdf)

# 数据集/Database
* [MASSIVE MIMO CSI MEASUREMENTS](https://homes.esat.kuleuven.be/~sdebast/csi_measurements.html)
* [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)
* [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)。
> 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](https://www.deepsig.io/datasets)
* [open datase](https://pan.baidu.com/s/1rS143bEDaOTEiCneXE67dg#list/path=%2F):来自论文[Signal Demodulation With Machine Learning
Methods for Physical Layer Visible Light
Communications: Prototype Platform,
Open Dataset, and Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8661606&tag=1)
>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
* [Ahmed Alkhateeb](http://www.aalkhateeb.net/index.html):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](https://ebjornson.com/research/):
  He performs research on multi-antenna communications, Massive MIMO, radio resource allocation, energy-efficient communications, and network design. 
* [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.
# 有用的网页和材料/Useful Websites and Materials
* [机器学习和通信结合论文列表/Research Library ](https://mlc.committees.comsoc.org/research-library/)
* [Best Readings in Machine Learning in Communications](https://www.comsoc.org/publications/best-readings/machine-learning-communications)


<br>贡献者/Contributors：

* WxZhu:
  - [Github](https://github.com/zhuwenxing)  
  - Email:wenxingzhu@shu.edu.cn
* [LinTian](https://github.com/TianLin0509)
* [HongtaiChen](https://github.com/HongtaiChen)
* [yihanjiang](https://github.com/yihanjiang)
* wu huaming:
  - Email:whming@tju.edu.cn

<br>版本更新/Version Update：

1. 第一版完成/First Version：2019-02-21