Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning approach to optimize resource allocation in mobile edge generation and computing systems, aiming to minimize latency and improve service quality for mobile users.

Contribution

It introduces a novel DRL-based algorithm for joint communication, computation, and AI-generated content resource allocation in MEGC systems, addressing the complex coupling of variables.

Findings

Achieves lower latency than baseline algorithms

Effectively optimizes joint resource allocation

Enhances quality of service for mobile users

Abstract

Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms.