Optimizing Computation Offloading in 6G Multi-Access Edge Computing Using Deep Reinforcement Learning - Saeed, Mamoon M.; Saeed, Rashid A.; Elshafie, Hashim; Awouda, Ala Eldin; E. Ahmed, Zeinab; A. Ahmed, Mayada; A Mokhtar, Rania (International Journal of Electrical and Computer Engineering Systems, 2025)
One of the most important technologies for future mobile networks is multi-access edge computing (MEC). Computational duties can be redirected to edge servers rather than distant cloud servers by placing edge computing facilities at the edge of the wireless access network. This will meet the needs of 6G applications that demand high reliability and low latency. At the same time, as wireless network technology develops, a variety of computationally demanding and time-sensitive 6G applications appear. These jobs require lower latency and higher processing priority than traditional internet operations. This study presents a 6G multi-access edge computing network design to reduce total system costs, creating a collective optimization challenge. To tackle this problem, Joint Computation Offloading and Task Migration Optimization (JCOTM), an approach based on deep reinforcement learning, is presented. This algorithm takes into consideration several factors, such as the allocation of system computing resources, network communication capacity, and the simultaneous execution of many calculation jobs. A Markov Decision Process is used tosimulate the mixed integer nonlinear programming problem. The effectiveness of the suggested algorithm in reducing equipment energy consumption and task processing delays is demonstrated by experimental findings. Compared to other computing offloading techniques, it maximizes resource allocation and computing offloading methodologies, improving system resource consumption. The presented findings are based on a set of simulations done in TensorFlow and Python 3.7 for the Joint Computation Offloading and Task Management (JCOTM) method. Changing key parameters lets us find out that the JCOTM algorithm does converge, with rewards providing a measure of its success compared to various task offloading methods. 15 users and 4 RSUs are placed in the MEC network which faces resource shortages and is aware of users. According to the tests, JCOTM offers a lower average system offloading cost than local, edge, cloud, random computing and a game-theory-based technique. When there are more users and data, JCOTM continues to manage resources effectively and shows excellent speed in processing demands. It can be seen from these results that JCOTM makes it possible to offload efficiently as both server loads and user needs change in MEC environments.