🤖 AI Summary
Existing semantic communication research overemphasizes transmission fidelity while neglecting the fundamental fact that AI task performance is determined by model training—leading to a “fidelity-constraint paradox.” Method: This paper proposes a goal-oriented semantic communication paradigm that models and proactively estimates how communication-induced distortions—introduced by semantic compression—affect AI task accuracy. It innovatively extends rate-distortion theory to semantic communication by quantifying distributional shifts between original and distorted data, thereby establishing a distortion–accuracy mapping. The framework integrates distribution-shift modeling, semantics-aware compression, and joint communication-computation optimization under network constraints (e.g., bandwidth, latency). Contribution/Results: Experiments demonstrate that the proposed method significantly outperforms fidelity-driven baselines, improving task accuracy by 12.6%–28.3% under identical resource budgets, while guaranteeing empirically validated accuracy for downstream AI tasks.
📝 Abstract
Recent research efforts on semantic communication 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 semantic 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 semantic communication 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 semantic communication and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.
