2024/01/10 by Aftab, Asad, Rehman, Semeen
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2402.00874
Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs during task processing. In this paper, we proposed a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading that enables a green environment. Our optimization problem is NP-hard; thus, we proposed a decentralized Reinforcement Learning (dRL) approach where we eliminate the problem of dimensionality and over-estimation of the value functions. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs.