Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning
This work addresses latency reduction for mobile users in edge computing and AI-generated content services, but it appears incremental as it applies an existing method to a new integration area.
The paper tackled the joint resource allocation problem in mobile edge generation and computing systems to minimize latency for mobile users, and the proposed deep reinforcement learning algorithm achieved lower latency compared to two baseline methods.
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.