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

**Repository Path**: conquerzheng/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**: 0
- **Created**: 2019-03-27
- **Last Updated**: 2020-12-19

## 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^_^

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                                                        | Code                                                         |
| ------------------------------------------------------------ | ------------------------------------------------------------ |
|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) |
| 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) |
| 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)        |

# 数据集
* [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)

<br>贡献者：

WxZhu:
* [Github](https://github.com/zhuwenxing)  
* Email:wenxingzhu@shu.edu.cn

版本更新：

1. 第一版完成：2019-02-21