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.

翻译：近期关于语义通信的研究大多将精度作为优化目标导向通信系统的主要问题。然而，这些方法引入了一个悖论：人工智能任务的准确性应通过训练自然涌现，而非受限于网络约束。针对这一困境，本文提出了一种创新方法，利用率失真理论分析通信与语义压缩引入的失真，进而分析学习过程。具体而言，我们考察原始数据与失真数据之间的分布偏移，从而评估其对AI模型性能的影响。基于此分析，我们可以预先估计AI任务的经验准确性，使目标导向的语义通信问题变得可行。为实现这一目标，我们阐述了该方法理论基础，并通过仿真与实验验证其有效性。实验结果表明，所提方法在满足网络约束的同时能够实现准确的AI任务性能，为信号处理领域做出了重要贡献。此外，本工作推进了目标导向语义通信的研究，并突显了数据驱动方法在优化智能系统性能中的重要意义。