Joint Trajectory and Resource Optimization of MEC-Assisted UAVs in Sub-THz Networks: A Resources-Based Multi-Agent Proximal Policy Optimization DRL With Attention Mechanism
Yu Min Park, Sheikh Salman Hassan, Yan Kyaw Tun, Zhu Han, Choong Seon Hong
Abstract
The use of Terahertz (THz) technology in sixth-generation (6G) networks will bring high-speed and capacity data services. But limitations like molecular absorption, rain attenuation, and limited coverage range cause communication losses. To overcome these losses and improve coverage in rural areas, a high number of base stations are required. Hence, an aerial communication platform, which uses line-of-sight (LoS) communication to avoid losses, is needed. To address this, we study the deployment and optimization of multi-access edge computing (MEC)-powered unmanned aerial vehicle (UAV) for sub-THz communication in remote areas. To this end, we solve an optimization problem to minimize energy consumption and delay for MEC-UAV and mobile users. The formulated problem is a mixed-integer non-linear programming (MINLP) problem. As the problem is an MINLP, we decompose the main problem into two subproblems. Due to its convex nature, we solve the first subproblem with a standard optimization solver, i.e., CVXPY. To solve the second subproblem, we design a resources-based multi-agent proximal policy optimization (RMAPPO) deep reinforcement learning (DRL) algorithm with an attention mechanism. The considered attention mechanism is utilized for encoding a diverse number of observations. This is designed by the network coordinator to provide a differentiated fit reward to each agent in the network. The simulation results show that the proposed algorithm outperforms the benchmark and yields a network utility that is 2.22%, 15.55%, and 17.77% more than the benchmarks.