Distortion Resilience for Goal-Oriented Semantic Communication

TL;DR

This paper introduces a rate distortion theory-based approach to analyze and predict the impact of communication-induced distortions on AI task accuracy in goal-oriented semantic communication systems, balancing performance and network constraints.

Contribution

It presents a novel theoretical framework that links distortion analysis with AI performance prediction, advancing goal-oriented SemCom research beyond accuracy-focused methods.

Findings

Effective estimation of AI task accuracy under communication distortions

Demonstrated the approach's ability to optimize performance within network constraints

Validated through simulations and experiments showing practical benefits

Abstract

Recent research efforts on Semantic Communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of Artificial Intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate distortion theory to analyze distortions induced by communication and compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model's performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented SemCom problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented SemCom and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.