Channel model for end-to-end learning of communications systems: A survey

2022/04/08 by Ijaz Ahmad, Ahmad, Ijaz, Seokjoo Shin +1
Computer Science · #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Networking and Internet Architecture (cs.NI) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2204.03944

openalex publication_date 2022/04/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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