# Deep Learning for Wireless Communications

arXiv: https://arxiv.org/abs/2005.06068 · HTML: https://mlsanity.com/paper/2005.06068/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 30 of 39)

- [Teacher-Student Architecture for Knowledge Distillation: A Survey](https://mlsanity.com/paper/2308.04268/) (2023)
- [Teacher-Student Architecture for Knowledge Learning: A Survey](https://mlsanity.com/paper/2210.17332/) (2022)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2207.03046/) (2022)
- [Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning](https://mlsanity.com/paper/2409.00124/) (2024)
- [Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks](https://mlsanity.com/paper/2204.03027/) (2022)
- [Joint Sensing and Semantic Communications with Multi-Task Deep Learning](https://mlsanity.com/paper/2311.05017/) (2023)
- [Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing](https://mlsanity.com/paper/2101.08724/) (2021)
- [Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond](https://mlsanity.com/paper/2103.13989/) (2021)
- [Auto-encoder based Model for High-dimensional Imbalanced Industrial Data](https://mlsanity.com/paper/2108.02083/) (2021)
- [Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networks](https://mlsanity.com/paper/2212.11194/) (2022)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning](https://mlsanity.com/paper/2107.12173/) (2021)
- [Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning](https://mlsanity.com/paper/2308.06884/) (2023)
- [Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications](https://mlsanity.com/paper/2109.08139/) (2021)
- [Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks](https://mlsanity.com/paper/2301.05250/) (2023)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [Deep Learning Based Uplink Multi-User SIMO Beamforming Design](https://mlsanity.com/paper/2309.16603/) (2023)
- [Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks](https://mlsanity.com/paper/2101.01847/) (2021)
- [Autoencoder-based Communications with Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.04441/) (2021)
- [Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Access](https://mlsanity.com/paper/2312.13931/) (2023)
- [Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities](https://mlsanity.com/paper/2509.06968/) (2025)
- [End-to-End Autoencoder Communications with Optimized Interference Suppression](https://mlsanity.com/paper/2201.01388/) (2021)
- [Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution](https://mlsanity.com/paper/2312.17164/) (2023)
- [Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning](https://mlsanity.com/paper/2411.10385/) (2024)
- [Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training](https://mlsanity.com/paper/2206.06592/) (2022)
- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)
- [Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2212.11778/) (2022)
- [Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing](https://mlsanity.com/paper/2212.13598/) (2022)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)

## Abstract (arXiv)

Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom. Deep learning has a strong potential to overcome this challenge via data-driven solutions and improve the performance of wireless systems in utilizing limited spectrum resources. In this chapter, we first describe how deep learning is used to design an end-to-end communication system using autoencoders. This flexible design effectively captures channel impairments and optimizes transmitter and receiver operations jointly in single-antenna, multiple-antenna, and multiuser communications. Next, we present the benefits of deep learning in spectrum situation awareness ranging from channel modeling and estimation to signal detection and classification tasks. Deep learning improves the performance when the model-based methods fail. Finally, we discuss how deep learning applies to wireless communication security. In this context, adversarial machine learning provides novel means to launch and defend against wireless attacks. These applications demonstrate the power of deep learning in providing novel means to design, optimize, adapt, and secure wireless communications.