Resource Allocation for Semantic-Aware Mobile Edge Computing Systems

TL;DR

This paper introduces a semantic-aware resource allocation framework for mobile edge computing that reduces delays by optimizing semantic data transmission and processing, outperforming traditional methods.

Contribution

It proposes a novel joint semantic-aware resource allocation model and derives a closed-form solution, improving delay performance in MEC systems.

Findings

Achieves up to 37.10% delay reduction.

Small semantic extraction factors are optimal for large tasks and poor channels.

Transforms a non-convex problem into a convex one for efficient solutions.

Abstract

In this paper, a semantic-aware joint communication and computation resource allocation framework is proposed for mobile edge computing (MEC) systems. In the considered system, each terminal device (TD) has a computation task, which needs to be executed by offloading to the MEC server. To further decrease the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the maximum execution delay of all TDs while satisfying energy consumption constraints. The original non-convex problem is transformed into a convex one based on the geometric programming and the optimal solution is obtained by the alternating optimization algorithm. Moreover, the closed-form optimal solution of the semantic extraction factor is derived. Simulation results show that the proposed algorithm yields up to 37.10% delay reduction compared with the benchmark algorithm without semantic-aware allocation. Furthermore, small semantic extraction factors are preferred in the case of large task sizes and poor channel conditions.