Joint Semantic Information Extraction and Resource Allocation in User-Centric Semantic Communication Networks

Semantic communication (SemCom) has recently emerged as a transformative paradigm for next-generation wireless systems, aiming to enable task-oriented information transmission. However, existing SemCom networks rely on cell-centric architectures, where users, particularly those at the cell edge, suffer from severe inter-cell interference and poor channel conditions. These issues undermine semantic accuracy, induce intolerable delay, and ultimately hinder the fulfillment of quality-of-service (QoS) requirements. To overcome these limitations, we propose the user-centric SemCom (UCSC) network, a novel architecture that integrates user-centric networks with semantic-aware transmission. In UCSC, each user is served by a dedicated cooperative access point group, enabling effective interference mitigation and reliable semantic delivery, even at the cell edge. To fully exploit the potential of UCSC, we formulate a joint optimization problem for semantic information extraction and resource allocation, aiming to minimize total energy consumption under constraints on semantic similarity and delay. By decomposing the original problem into three tractable subproblems, semantic information extraction, computational capacity control, and power allocation, we propose an alternating optimization algorithm to efficiently solve it. Simulation results demonstrate that UCSC, combined with the proposed algorithm, achieves significant performance improvements in reducing energy consumption compared with cell-centric SemCom networks, especially under stringent QoS requirements.