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Recent research efforts on semantic communication have mostly consideredaccuracy as a main problem for optimizing goal-oriented communication systems.However, these approaches introduce a paradox: the accuracy of artificialintelligence (AI) tasks should naturally emerge through training rather thanbeing dictated by network constraints. Acknowledging this dilemma, this workintroduces an innovative approach that leverages the rate-distortion theory toanalyze distortions induced by communication and semantic compression, therebyanalyzing the learning process. Specifically, we examine the distribution shiftbetween the original data and the distorted data, thus assessing its impact onthe AI model's performance. Founding upon this analysis, we can preemptivelyestimate the empirical accuracy of AI tasks, making the goal-oriented semanticcommunication problem feasible. To achieve this objective, we present thetheoretical foundation of our approach, accompanied by simulations andexperiments that demonstrate its effectiveness. The experimental resultsindicate that our proposed method enables accurate AI task performance whileadhering to network constraints, establishing it as a valuable contribution tothe field of signal processing. Furthermore, this work advances research ingoal-oriented semantic communication and highlights the significance ofdata-driven approaches in optimizing the performance of intelligent systems.

Joint Communication and Computation Framework for Goal-Oriented Semantic Communication with Distortion Rate Resilience