Data to 7 Oct 2026

An Introduction to Deep Learning for the Physical Layer

arXiv 1702.00832 ↗First posted 2 Feb 2017cs.IT cs.LG cs.NIcited by 227

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Abstract (arXiv)

We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how this idea can be extended to networks of multiple transmitters and receivers and present the concept of radio transformer networks as a means to incorporate expert domain knowledge in the machine learning model. Lastly, we demonstrate the application of convolutional neural networks on raw IQ samples for modulation classification which achieves competitive accuracy with respect to traditional schemes relying on expert features. The paper is concluded with a discussion of open challenges and areas for future investigation.

Key facts. Cited by 227 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 50; 2022: 45; 2023: 39; 2024: 34; 2025: 27; 2026: 32.

227 papers.

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2022Sionna: An Open-Source Library for Next-Generation Physical Layer Researchcited by 143
2021Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approachcited by 105
2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
2022Less Data, More Knowledge: Building Next Generation Semantic Communication Networkscited by 61
2022Robust Semantic Communications with Masked VQ-VAE Enabled Codebookcited by 53
2022Overview of Deep Learning-based CSI Feedback in Massive MIMO Systemscited by 35
2021Deep Joint Source Channel Coding for WirelessImage Transmission with OFDMcited by 22
2022Task-Oriented Communications for NextG: End-to-End Deep Learning and AI Security Aspectscited by 20
2023Joint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Schemecited by 19
2021Transfer Learning for Future Wireless Networks: A Comprehensive Surveycited by 18
2021Decentralized Inference with Graph Neural Networks in Wireless Communication Systemscited by 18
2023Generative AI for Physical Layer Communications: A Surveycited by 17

38 more

2021KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learningcited by 16
2021Reinforcement Learning-powered Semantic Communication via Semantic Similaritycited by 15
2021An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformationcited by 15
2021ProductAE: Towards Training Larger Channel Codes based on Neural Product Codescited by 14
2021Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspectivecited by 12
2022Semantic Information Recovery in Wireless Networkscited by 12
2021Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluationcited by 11
2021Waveform Learning for Next-Generation Wireless Communication Systemscited by 10
2021Interference Suppression Using Deep Learning: Current Approaches and Open Challengescited by 10
2022Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel chartingcited by 10
2022Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communicationcited by 10
2022Graph Neural Networks for Channel Decodingcited by 10
2022Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attackscited by 10
2022Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attackscited by 10
2022MIMO-GAN: Generative MIMO Channel Modelingcited by 9
2023Age of Information in Deep Learning-Driven Task-Oriented Communicationscited by 9
2023Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Frameworkcited by 9
2021Efficient channel charting via phase-insensitive distance computationcited by 8
2022Efficient Deep Unfolding for SISO-OFDM Channel Estimationcited by 8
2022Semantics-Empowered Communication: A Tutorial-cum-Surveycited by 8
2023Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systemscited by 8
2021Privacy-Aware Communication Over a Wiretap Channel with Generative Networkscited by 7
2021Deep learning for location based beamforming with NLOS channelscited by 7
2024Enhancing Automatic Modulation Recognition through Robust Global Feature Extractioncited by 7
2024Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneckcited by 7
2021Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learningcited by 6
2021A Channel Coding Benchmark for Meta-Learningcited by 6
2022A Survey on Deep Learning based Channel Estimation in Doubly Dispersive Environmentscited by 6
2023How to Evaluate Semantic Communications for Images with ViTScore Metric?cited by 6
2024Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applicationscited by 6
2021CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communicationscited by 5
2021SVD-Embedded Deep Autoencoder for MIMO Communicationscited by 5
2022Waveform Learning for Reduced Out-of-Band Emissions Under a Nonlinear Power Amplifiercited by 5
2022DeepTx: Deep Learning Beamforming with Channel Predictioncited by 5
2022Artificial Intelligence Techniques for Next-Generation Mega Satellite Networkscited by 5
2022Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networkscited by 5
2022CRISP: Curriculum based Sequential Neural Decoders for Polar Code Familycited by 5
2022Channel charting based beamformingcited by 5

50 most cited of 227 All 227 (JSON)