Channel model for end-to-end learning of communications systems: A survey
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Abstract (arXiv)
The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 8 Apr 2022.
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References · 9
- 2017An Introduction to Deep Learning for the Physical Layercited by 233
- 2017Deep Learning-Based Communication Over the Aircited by 72
- 2016Learning to Decode Linear Codes Using Deep Learningcited by 61
- 2018Model-free Training of End-to-end Communication Systemscited by 30
- 2018Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networkscited by 25
- 2018End-to-End Learning of Communications Systems Without a Channel Modelcited by 21
- 2016Learning to Protect Communications with Adversarial Neural Cryptographycited by 11
- 2016Unsupervised Representation Learning of Structured Radio Communication Signalscited by 7
- 2017RNN Decoding of Linear Block Codescited by 3
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