Joint optimization of computation offloading and power control in user-centric networks based on dual layer mobile edge computing

Abstract

In mobile edge computing (MEC) over traditional cellular networks (TCN), users located at the cell edges are prone to severe edge interference and signal attenuation, leading to low throughput and even transmission interruptions. These edge effects significantly hinder the offloading of computational tasks from end devices to MEC servers, adversely affecting the user equipment (UE) experience. To address these issues and improve UE experience within the network, we design a user-centric network (UCN) structure comprising a three-tier network interconnected through communication links to ensure low latency and reliable transmission during computation offloading. For the computation offloading problem within UCN, we design a network model with dual-layer MEC servers that considers the competitive nature of UEs in real-world scenarios. This model is solved using a multi-agent reinforcement learning algorithm to optimize network strategies, achieving minimal long-term average total delay and power consumption. Experimental results demonstrate that the proposed scheme significantly reduces system power consumption, with a maximum reduction of 36.19% compared to other baseline algorithms. The scheme also achieves substantial reduction in the long-term average total delay by 39.5%. These algorithms indicate that the proposed approach offers considerable advantages in enhancing UE experience and reducing the energy consumption.