Knowledge Sharing-Enabled Semantic Rate Maximization for Multi-Cell Task-Oriented Hybrid Semantic-Bit Communication Networks

In task-oriented semantic communications, the transmitters are designed to deliver task-related semantic information rather than every signal bit to receivers, which alleviates the spectrum pressure by reducing network traffic loads. Effective semantic communications depend on the perfect alignment of shared knowledge between transmitters and receivers, however, the knowledge alignment cannot always be guaranteed in practice. In multi-cell networks, due to heterogeneous transceivers with distinct knowledge bases and limited computation capabilities, and random channel conditions in between, it is challenging for mobile devices (MDs) to access the best small base station (SBS) to perform effective semantic communications and complete requested tasks. To address the knowledge mismatch issue, we propose a novel task-oriented semantic transmission mechanism, leveraging knowledge sharing and bit communications to guarantee the effective target task execution. To maximize the derived semantic-based performance metric, i.e., generalized effective semantic transmission rate of all MDs under the designed mechanism, a mixed integer nonlinear programming problem is formulated to jointly optimize knowledge sharing decisions, semantic extraction ratios, and SBS associations while satisfying the semantic accuracy and delay requirements of target tasks. By decomposing the formulated problem into multiple subproblems equivalently, an optimum algorithm is proposed and another efficient algorithm is further developed using hierarchical class partitioning and monotonic optimization. A variety of simulation results demonstrate the validity and excellent performance of proposed solutions over a wide range of system parameters.