  Semantic communications offer the potential to alleviate communication loads
by exchanging meaningful information. However, semantic extraction (SE) is
computationally intensive, posing challenges for resource-constrained Internet
of Things (IoT) devices. To address this, leveraging computing resources at the
edge servers (ESs) is essential. ESs can support multiple SE models for various
tasks, making it crucial to select appropriate SE models based on diverse
requirements of IoT devices and limited ES computing resources. In this letter,
we study an SE model selection problem, where the ES co-located at the access
point can provide multiple SE models to execute the uploaded SE tasks of
associated IoT devices. We aim to maximize the total semantic rate of all SE
tasks by selecting appropriate SE models, while considering SE delay and ES
capacity constraints, and SE accuracy requirements. The formulated NP-complete
integer programming problem is transformed into a modified Knapsack problem.
The proposed efficient approximation algorithm using dynamic programming can
yield a guaranteed near-optimum solution. Simulation results demonstrate the
superior performance of proposed solution.
