Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications

Task-oriented communication is a new paradigm that aims at providing efficient connectivity for accomplishing intelligent tasks rather than reception of every transmitted bit. This paper proposes task-oriented communication architecture for end-to-end 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, we propose a compression ratio and resource allocation (CRRA) algorithm that separates the problem into two subproblems and solving them iteratively. Furthermore, for scenarios with varying service levels among users, a compression ratio, resource allocation, and user selection (CRRAUS) algorithm is proposed, adaptively selecting users through branch and bound method. Simulation results show that ASC approach can reduce the size of transmitted data by up to 80% without compromising task success probability. Furthermore, numerical results clearly demonstrate that both the proposed CRRA and CRRAUS algorithms lead to substantial improvements in terms of success gains when compared to the baselines.