Work

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

Year: 2024

Type: article

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 several baseline algorithms.

Source: IEEE Networking Letters

DOI: 10.1109/lnet.2024.3486194

Authors: Yinyu Wu, Xuhui Zhang, Jinke Ren, Huijun Xing, Yanyan Shen, Shuguang Cui

Institutions: Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Chinese University of Hong Kong, Chinese University of Hong Kong, Shenzhen

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: American Indian Graduate Center, National Natural Science Foundation of China, Chuzhou Science and Technology Program, Key Laboratory of System Control and Information Processing