Channel model for end-to-end learning of communications systems: A survey
This is an incremental survey that summarizes methods for a domain-specific problem in communications systems, targeting researchers in that field.
The paper surveys existing approaches to address the limitation of requiring a differentiable channel model in end-to-end learning of communications systems, which aims to optimize system metrics jointly across components for improved performance.
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.