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）

论文

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

数据集

• 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.

学者个人主页

• 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:[email protected]

版本更新：

1. 第一版完成：2019-02-21