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

TL;DR

This survey reviews end-to-end learning approaches for communication systems, highlighting their advantages over traditional block-based methods and discussing solutions to the differentiability challenge in channel modeling.

Contribution

It provides a comprehensive overview of existing methods that enable end-to-end learning despite the non-differentiable nature of communication channels.

Findings

Summarizes approaches to handle non-differentiable channels

Highlights performance improvements of end-to-end learning

Identifies open challenges and future research directions

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.