mlsanityData to 7 Oct 2026

Deep Learning for Wireless Communications

arXiv 2005.06068 ↗First posted 12 May 2020cs.NI cs.LGcited by 39

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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.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2020.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 9; 2024: 6; 2025: 4.

39 papers.

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  1. 2023Teacher-Student Architecture for Knowledge Distillation: A Surveycited by 26
  2. 2022Teacher-Student Architecture for Knowledge Learning: A Surveycited by 23
  3. 2021Adversarial Machine Learning for 5G Communications Securitycited by 12
  4. 2022Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learningcited by 11
  5. 2024Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learningcited by 9
  6. 2022Federated Learning for Distributed Spectrum Sensing in NextG Communication Networkscited by 7
  7. 2023Joint Sensing and Semantic Communications with Multi-Task Deep Learningcited by 7
  8. 2021Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicingcited by 6
  9. 2021Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyondcited by 6
  10. 2021Auto-encoder based Model for High-dimensional Imbalanced Industrial Datacited by 6
  11. 2022Free-Rider Games for Federated Learning with Selfish Clients in NextG Wireless Networkscited by 6
  12. 2024Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applicationscited by 6
27 more
  1. 2023Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learningcited by 5
  2. 2021Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learningcited by 4
  3. 2021Adversarial Attacks against Deep Learning Based Power Control in Wireless Communicationscited by 4
  4. 2023Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networkscited by 4
  5. 2023Generalization Bounds for Neural Belief Propagation Decoderscited by 4
  6. 2023Deep Learning Based Uplink Multi-User SIMO Beamforming Designcited by 4
  7. 2021Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networkscited by 3
  8. 2021Autoencoder-based Communications with Reconfigurable Intelligent Surfacescited by 3
  9. 2023Joint Sensing and Task-Oriented Communications with Image and Wireless Data Modalities for Dynamic Spectrum Accesscited by 3
  10. 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunitiescited by 3
  11. 2021End-to-End Autoencoder Communications with Optimized Interference Suppressioncited by 2
  12. 2023Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solutioncited by 2
  13. 2024Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learningcited by 2
  14. 2022Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Trainingcited by 1
  15. 2022Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systemscited by 1
  16. 2022Adversarial Machine Learning and Defense Game for NextG Signal Classification with Deep Learningcited by 1
  17. 2022Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharingcited by 1
  18. 2023Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learningcited by 1
  19. 2024Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Designcited by 1
  20. 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learningcited by 1
  21. 2025Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channelscited by 1
  22. 2021Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfacescited by 0
  23. 2022Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Priorcited by 0
  24. 2022Calibrating AI Models for Wireless Communications via Conformal Predictioncited by 0
  25. 2024Learning Short Codes for Fading Channels with No or Receiver-Only Channel State Informationcited by 0
  26. 2025On Inverse Problems, Parameter Estimation, and Domain Generalizationcited by 0
  27. 2025Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environmentscited by 0