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）近期被视为一种有前景的解决方案，可确保未来无线网络的高资源利用率和传输可靠性。然而，由于对背景知识匹配的特殊需求，在支持SemCom的网络（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SemCom，系统性地解决SC-Net中用户关联（UA）和带宽分配（BA）两个基础问题。首先，考虑移动用户与关联基站之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和基于不完美知识匹配的SC-Net。随后，针对每种SC-Net场景，从语义信息理论角度描述其独特的语义信道模型，由此提出比特速率到消息速率的转换概念，并引入新的语义层级指标——系统消息吞吐量（STM）来衡量整体网络性能。在此基础上，针对每种SC-Net场景构建了UA和BA的联合STM最大化问题，并相应提出最优求解方案。两种场景下的数值结果表明，与两种基准方案相比，我们的方案在STM性能上具有显著优越性和可靠性。