无线与深度学习结合的论文代码整理
资源内容介绍
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。 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，非常欢迎添加在issue当中**，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 - [x] 按不同小方向分类- [x] 论文添加下载链接- [x] 增加更多相关论文代码 * 在[daily_arxiv](https://github.com/zhuwenxing/daily_arxiv)这个repo下会以daily为尺度更新`eess.SP`和`cs.IT`分类下开源的代码论文 * 该Repo通过爬虫+Github Action实现每日自动更新- [ ] 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）## 目录 （Contents)- [Topics](#topics) + [Machine/deep learning for physical layer optimization](#physical-layer-optimization) + [Resource, power and network optimization using machine learning techniques](#resource-and-network-optimization) + [Distributed learning algorithms over communication networks](#distributed-learning-algorithms-over-communication-networks) + [Multiple access scheduling and routing using machine learning techniques](#multiple-access-scheduling--and-routing-using-machine-learning-techniques) + [Machine learning for network slicing, network virtualization, and software-defined networking](#machine-learning-for--software-defined-networking) + [Machine learning for emerging communication systems and applications (e.g., IoT, edge computing, caching, smart cities, vehicular networks, and localization)](#machine-learning-for-emerging-communication-systems-and-applications) + [Secure machine learning over communication networks](#secure-machine-learning-over-communication-networks)## Topics### Physical layer optimization| Paper | Code || ------------------------------------------------------------ | ------------------------------------------------------------ ||[Online Meta-Learning For Hybrid Model-Based Deep Receivers](https://arxiv.org/abs/2203.14359)|[meta-deepsic](https://github.com/tomerraviv95/meta-deepsic)||[Gan-Based Joint Activity Detection and Channel Estimation For Grant-free Random Access](https://arxiv.org/abs/2204.01731)|[jadce](https://github.com/deeeeeeplearning/jadce)||[sionna: an open-source library for next-generation physical layer research](https://arxiv.org/abs/2203.11854)|[sionna](https://github.com/nvlabs/sionna)||[Deep Learning Aided Robust Joint Channel Classification, Channel Estimation, and Signal Detection for Underwater Optical Communication](https://ieeexplore.ieee.org/document/9302692)|[UWOC-JCCESD](https://github.com/Huaiyin-Lu/UWOC-JCCESD)||[LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers](https://arxiv.org/abs/2102.02993)|[LoRD-Net](https://github.com/skhobahi/LoRD-Net)||[Deep Diffusion Models for Robust Channel Estimation](https://arxiv.org/abs/2111.08177)|[diffusion-channels](https://github.com/utcsilab/diffusion-channels)||[A Channel Coding Benchmark for Meta-Learning](https://openreview.net/forum?id=DjzPaX8AT0z)|[MetaCC](https://github.com/ruihuili/MetaCC)||[On the Feasibility of Modeling OFDM Communication Signals with Unsupervised Generative Adversarial Networks](https://arxiv.org/abs/2109.05107)|[OFDM-GAN](https://github.com/usnistgov/OFDM-GAN)||[Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals](https://ieeexplore.ieee.org/document/9013332)|[LearningML](https://github.com/Yunseong-Cho/LearningML)||[iterative error decimation for syndrome-based neural network decoders](https://arxiv.org/abs/2012.00089)|[ied](https://github.com/kamassury/ied)||[ko codes: inventing nonlinear encoding and decoding for reliable wireless communication via deep-learning](https://arxiv.org/abs/2108.12920)|[kocodes](https://github.com/deepcomm/kocodes)||[Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications](https://arxiv.org/abs/2009.01423)|[CDRN-channel-estimation-IRS](https://github.com/XML124/CDRN-channel-estimation-IRS)||[Model-Driven Deep Learning for MIMO Detection](https://ieeexplore.ieee.org/document/9018199)|[OAMP-Net](https://github.com/hehengtao/OAMP-Net)||[Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems](https://arxiv.org/abs/2106.04043)|[DCRNet](https://github.com/recusant7/DCRNet)||[Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming](https://arxiv.org/abs/2007.00038)|[HBF-Net](https://github.com/HamedHojatian/HBF-Net)||[CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback](https://arxiv.org/abs/2102.07507)|[CLNet](https://github.com/SIJIEJI/CLNet)||[Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation](https://arxiv.org/abs/2008.03612)|[B_DNN](https://github.com/hasanabs/B_DNN)||[Deep Active Learning Approach to Adaptive Beamforming for mmWave Initial Alignment](https://arxiv.org/abs/2012.13607)|[DL-ActiveLearning-BeamAlignment](https://github.com/foadsohrabi/DL-ActiveLearning-BeamAlignment)||[Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO](https://ieeexplore.ieee.org/document/9037126)|[Source-Code-X.Ma](https://github.com/gaozhen16/Source-Code-X.Ma)||[Deep Learning Predictive Band Switching in Wireless Networks](https://arxiv.org/abs/1910.05305)|[Bandswitch-DeepMIMO](https://github.com/farismismar/Bandswitch-DeepMIMO)||[RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection](https://arxiv.org/abs/2007.00140)|[RE-MIMO](https://github.com/krpratik/RE-MIMO)||[NOLD: A Neural-Network Optimized Low-Resolution Decoder for LDPC Codes](https://github.com/Leo-Chu/NOLD/blob/master/JCN20-DIV2-067.R2.pdf)|[NOLD](https://github.com/Leo-Chu/NOLD)||[A MIMO detector with deep learning in the presence of correlated interference](https://ieeexplore.ieee.org/abstract/document/8990045)|[project_dcnnmld](https://github.com/skypitcher/project_dcnnmld)||[Deep Learning Driven Non-Orthogonal Precoding for Millimeter Wave Communications](https://ieeexplore.ieee.org/document/9082619)|[Deep-Learning-Driven-Non-Orthogonal-Precoding-for-Millimeter-Wave-Communications](https://github.com/JKLinUESTC/Deep-Learning-Driven-Non-Orthogonal-Precoding-for-Millimeter-Wave-Communications)||[Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9246287)|[DeepUnfolding_WMMSE](https://github.com/hqyyqh888/DeepUnfolding_WMMSE)|| [Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems](https://arxiv.org/pdf/1708.08514.pdf)| [haoyye/OFDM_DNN](https://github.com/haoyye/OFDM_DNN) || [Automatic Modulation Classification: A Deep Learning Enabled Approach](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8454504) | [mengxiaomao](https://github.com/mengxiaomao)/[CNN_AMC](https://github.com/mengxiaomao/CNN_AMC) || [Deep Architectures for Modulation Recognition](https://arxiv.org/pdf/1703.09197.pdf) | [qie