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

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.