Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this paper investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.

翻译：语义通信（SemCom）近期被视为保障未来无线网络高资源利用率和传输可靠性的有前景方案。然而，背景知识匹配的独特需求使得在语义通信网络（SC-Net）中实现多用户高效无线资源管理面临挑战。为此，本文从网络视角研究语义通信，系统性地解决SC-Net中用户关联（UA）与带宽分配（BA）这两个基础问题。首先，考虑移动用户与关联基站间的知识匹配状态差异，我们识别出两种通用SC-Net场景：基于完美知识匹配的SC-Net和基于非完美知识匹配的SC-Net。随后，针对每种SC-Net场景，我们从语义信息论角度描述其独特的语义信道模型，由此提出比特率-消息率转换概念，并引入新的语义级指标——消息级系统吞吐量（STM）来评估整体网络性能。基于此，我们分别为两种SC-Net场景构建了联合最大化STM的UA与BA优化问题，并相应提出最优解。两种场景下的数值结果均表明，与两种基准方案相比，我们的解决方案在STM性能上具有显著优越性和可靠性。