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，为community贡献一份力量。欢迎交流^_^
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）
| Paper | Code |
|---|---|
| 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 |
| RF-based Direction Finding of UAVs Using DNN | https://github.com/LahiruJayasinghe/DeepDOA |
| Deepcode: Feedback Codes via Deep Learning | https://github.com/hyejikim1/Deepcode 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 | caogang/WlanDqn |
| Deep-Learning-Power-Allocation-in-Massive-MIMO | lucasanguinetti / Deep-Learning-Power-Allocation-in-Massive-MIMO |
| DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications | The DeepMIMO Dataset |
| Fast Deep Learning for Automatic Modulation Classification | dl4amc/source |
| Deep Learning-Based Channel Estimation | Mehran-Soltani/ChannelNet |
| Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication | seotaijiya/TPC_D2D |
| Deep learning-based channel estimation for beamspace mmWave massive MIMO systems | hehengtao/LDAMP_based-Channel-estimation |
| Spatial deep learning for wireless scheduling | willtop/Spatial_DeepLearning_Wireless_Scheduling |
| Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach | swordest/mec_drl |
| A deep-reinforcement learning approach for software-defined networking routing optimization | 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 |
| 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 |
| 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 |
| DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls | ruihuili / DELMU |
| Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement | farismismar / Deep-Q-Learning-SON-Perf-Improvement |
| 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 |
| Convolutional Radio Modulation Recognition Networks | chrisruk/cnn qieaaa / Singal-CNN |
| Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks | zhongyuanzhao / dl_ofdm |
| Joint Transceiver Optimization for WirelessCommunication PHY with Convolutional NeuralNetwork | hlz1992/RadioCNN |
| Deep Learning for Massive MIMO CSI Feedback | sydney222 / Python_CsiNet |
| 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning | lasseufpa/5gm-data |
| Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks | shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access |
| DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning | zaxliu/deepnap |
| Automatic Modulation Classification: A Deep Learning Enabled Approach | mengxiaomao/CNN_AMC |
| Deep Architectures for Modulation Recognition | qieaaa / Deep-Architectures-for-Modulation-Recognition |
| 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 |
| Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems | haoyye/OFDM_DNN |
- 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.
- 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!
贡献者：
WxZhu:
- Github
- Email:wenxingzhu@shu.edu.cn
版本更新：
- 第一版完成：2019-02-21