Joint Cell Association and Spectrum Allocation in Semantic Communication Networks

Semantic communication (SemCom) has recently been considered a promising remedy to ensure high resource utilization and transmission reliability for next‐generation wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient resource management for multiple users in SemCom‐enabled networks (SC‐Nets). To this end, this chapter explores SemCom from a networking perspective, where two fundamental issues of cell association (CA) and spectrum allocation (SA) are systematically addressed in the SC‐Net. First, considering varying knowledge matching conditions between wireless users and 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 identify its distinctive semantic channel model from the semantic information theory perspective, and then a concept of bit‐rate‐to‐message‐rate transformation is developed alongside a new semantic‐level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we formulate a joint STM‐maximization problem of CA and SA for each SC‐Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both cases validate significant superiority and reliability of our solutions in the STM performance compared to benchmarks.