Page 86 - 2025S

UEC Int’l Mini-Conference No.54 79

Proximal Policy Optimization for Efficient D2D-assisted Computation

Offloading and Resource Allocation in Multi-Access Edge Computing

Chen Zhang, CelimugeWu
Department of Computer and Network Engineering
The University of Electro-Communications
Tokyo, Japan

Keywords: Multi-access edge computing (MEC); 5G networks; Device-to-Device (D2D); Proximal Policy Optimization
(PPO); Markov Decision Process (MDP); computation offloading; collaborative offloading; resource allocation.

Abstract

In advanced 5G and beyond networks, Multi-access Edge Computing (MEC) is increasingly
acknowledged as a promising technology, offering a dual advantage of reducing energy utilization in
cloud data centers while catering to the demands for reliability and real-time responsiveness in end
devices. However, the inherent complexity and variability of MEC networks pose significant
challenges in computational offloading decisions. Addressing this, we propose a Proximal Policy
Optimization (PPO)-based Device-to-Device (D2D) assisted computation offloading and resource
allocation scheme. We construct a realistic MEC network environment and develop a Markov Decision
Process (MDP) model that minimizes time loss and energy consumption. The integration of a D2D
communication-based offloading framework allows for collaborative task offloading between end
devices and MEC servers, enhancing both resource utilization and computational efficiency. The MDP
model is solved using the PPO algorithm in Deep Reinforcement Learning to derive an optimal policy
for offloading and resource allocation. Comparative analysis with three baseline approaches shows our
scheme's superior performance in latency, energy consumption, and algorithmic convergence,
demonstrating its potential in improving MEC network operations in the context of emerging 5G
technologies.

*The author is supported by (fee-exempted) MEXT Scholarship