Work

Queue Stability-Constrained Deep Reinforcement Learning Algorithms for Adaptive Transmission Control in Multi-Access Edge Computing Systems

Year: 2025

Type: article

Abstract: To meet the escalating demands of massive data transmission, the next generation of wireless networks will leverage the multi-access edge computing (MEC) architecture coupled with multi-access transmission technologies to enhance communication resource utilization. This paper presents queue stability-constrained reinforcement learning algorithms designed to optimize the transmission control mechanism in MEC systems to improve both throughput and reliability. We propose an analytical framework to model the queue stability. To increase transmission performance while maintaining queue stability, queueing delay model is designed to analyze the packet scheduling process by using the M/M/c queueing model and estimate the expected packet queueing delay. To handle the time-varying network environment, we introduce a queue stability constraint into the reinforcement learning reward function to jointly optimize latency and queue stability. The reinforcement learning algorithm is deployed at the MEC server to reduce the workload of central cloud servers. Simulation results validate that the proposed algorithm effectively controls queueing delay and average queue length while improving packet transmission success rates in dynamic MEC environments.

Source: Algorithms

DOI: 10.3390/a18080498

Authors: Longzhe Han, Tian Zeng, Jia Zhao, Xuecai Bao, Guangming Liu, Yan Liu

Institutions: Jiangxi University of Water Resources and Electric Power, Nanchang Institute of Technology, East China Normal University

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: gold

Estimated APC paid: $1,930