QoE-Aware Resource Allocation for Semantic Communication Networks

With the aim of accomplishing intelligence tasks, semantic communications transmit task-related information only, yielding significant performance gains over conventional communications. To guarantee user requirements for different tasks, we study the semantic-aware resource allocation in a multi-cell multi-task network in this paper. Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed. We formulate the QoE-aware resource allocation in terms of the number of transmitted semantic symbols, channel assignment, and power allocation. To solve this problem, we first decouple it into two independent subproblems. The first one is to optimize the number of transmitted semantic symbols with given channel assignment and power allocation, which is solved by the exhaustive search method. The second one is the channel assignment and power allocation subproblem, which is modeled as a many-to-one matching game and solved by the proposed low-complexity matching algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method on the overall QoE.

Automated summary

This paper investigates the problem of semantic-aware resource allocation in a multi-cell multi-task network. A novel measure of semantic entropy is proposed to quantify the semantic information for different tasks. A quality-of-experience (QoE) model is also proposed, and the resource allocation problem is formulated in terms of the number of transmitted semantic symbols, channel assignment, and power allocation. Simulation results demonstrate the effectiveness and superiority of the proposed method on the overall QoE.