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支持语义通信的网络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，系统性地解决了语义通信网络中的用户关联与带宽分配两个基本问题。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语义通信网络场景：基于完美知识匹配的语义通信网络与基于非完美知识匹配的语义通信网络。随后，针对每种场景，从语义信息论角度描述其独特的语义信道模型，在此基础上提出比特速率到消息速率的转换概念，并引入新的语义级性能指标——系统消息吞吐量（STM）——来评估整体网络性能。进而，针对每种语义通信网络场景，建立联合最大化STM的用户关联与带宽分配优化问题，并提出相应的最优解方案。两种场景下的数值结果表明，与两种基准方案相比，我们的解决方案在STM性能上具有显著的优越性和可靠性。