Online Optimization for Learning to Communicate over Time-Correlated Channels

TL;DR

This paper develops online optimization algorithms for learning to communicate over time-correlated channels, providing theoretical guarantees and demonstrating improved performance over baseline methods in simulations.

Contribution

It introduces online algorithms that handle non-i.i.d. channels, with theoretical regret bounds, advancing learning-based communication system design.

Findings

Algorithms achieve sub-linear regret bounds.

Simulation results show lower error rates with correlated channels.

Approach outperforms baseline methods in experiments.

Abstract

Machine learning techniques have garnered great interest in designing communication systems owing to their capacity in tackling with channel uncertainty. To provide theoretical guarantees for learning-based communication systems, some recent works analyze generalization bounds for devised methods based on the assumption of Independently and Identically Distributed (I.I.D.) channels, a condition rarely met in practical scenarios. In this paper, we drop the I.I.D. channel assumption and study an online optimization problem of learning to communicate over time-correlated channels. To address this issue, we further focus on two specific tasks: optimizing channel decoders for time-correlated fading channels and selecting optimal codebooks for time-correlated additive noise channels. For utilizing temporal dependence of considered channels to better learn communication systems, we develop two online optimization algorithms based on the optimistic online mirror descent framework. Furthermore, we provide theoretical guarantees for proposed algorithms via deriving sub-linear regret bound on the expected error probability of learned systems. Extensive simulation experiments have been conducted to validate that our presented approaches can leverage the channel correlation to achieve a lower average symbol error rate compared to baseline methods, consistent with our theoretical findings.