QoE-Driven Hybrid Resource Allocation for Multi-User OFDMA Semantic Communications

Abstract

In this paper, we develop a quality-of-experience (QoE)-driven hybrid resource allocation framework for multi-user downlink orthogonal frequency-division multiple access semantic communication systems, where the hybrid framework jointly handles continuous and discrete decision variables. Specifically, we formulate a mixed continuous-discrete optimization problem to jointly optimize transmit power, subcarrier allocation, and semantic decisions under coupled power and spectrum constraints. To better characterize semantic service quality, we propose a preference-inverse multiplicative exponential QoE that is bounded and non-compensatory, capturing the intrinsic ``both-required'' property of semantic rate and similarity. To solve the optimization problem, we develop a projection-based multi-agent deep deterministic policy gradient framework that enforces feasibility for continuous and discrete actions. Furthermore, we integrate Gumbel-softmax relaxation with an adaptive performance-driven temperature annealing scheme to enhance the learning of discrete semantic decisions. Simulation results demonstrate that our proposed framework achieves superior convergence, greater stability, and higher QoE compared to the conventional QoE design and penalty-based or fixed-temperature baselines, highlighting the benefits of our QoE design and the effectiveness of hybrid decision learning in semantic communications.