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.
TODO
- Sort by different subdirections
- Add download link to paper
- Add more related paper's code
- Traditional communication paper code list
- "Communication +DL" paper list (high cited, without code is ok)
| Paper | Code |
|---|---|
| DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications | The DeepMIMO Dataset |
| Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels | yihanjiang/turboae |
| Communication Algorithms via Deep Learning | yihanjiang/commviadl |
| 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 |
| Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning | TianLin0509/BF-design-with-DL |
| 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 |
Contributors：
Updates：
- complete first version ：2019-02-21