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与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基于语义通信的网络（SC-Nets）中为多用户实现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，系统解决了SC-Net中用户关联（UA）与带宽分配（BA）两个基本问题。首先，考虑移动用户与关联基站间知识匹配状态的差异性，我们识别出两种通用SC-Net场景：基于完美知识匹配的SC-Net与基于非完美知识匹配的SC-Net。随后，针对每种SC-Net场景，从语义信息论角度描述其特有的语义信道模型，据此提出比特率到消息率的转换概念，并引入新的语义层级性能指标——消息系统吞吐量（STM）来衡量整体网络性能。基于此，我们针对每个SC-Net场景构建了用户关联与带宽分配的联合STM最大化问题，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与可靠性。