  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.
