Performance Optimization for Task-Oriented Communications

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than the reception of every transmitted bit. This paper proposes a deep learning-based task-oriented communication architecture for end-to-end (E2E) semantics transmission, where extracted semantics is compressed by the proposed adaptable semantic compression (ASC) method. However, accommodating multiple users in a delay-intolerant system poses a challenge. Higher compression ratios conserve channel resources but cause semantic distortion, while lower ratios demand more resources and may lead to transmission failure due to delay constraints. To address this, we optimize both compression ratio and resource allocation to maximize task success probability. Specifically, due to the nonconvexity of the problem, we propose a compression ratio and resource allocation (CRRA) algorithm that separates the problem into two subproblems and solving them iteratively. Simulation results show that the proposed algorithm can obtain at least 14.3% success gains over baseline algorithms.