Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks

TL;DR

This paper presents a deep reinforcement learning approach to optimize resource allocation in active RIS-integrated TN-NTN networks, improving sum rate, energy efficiency, and outage performance through joint UAV, RIS, and power control.

Contribution

It introduces a novel hybrid proximal policy optimization algorithm for joint optimization of UAV trajectory, RIS parameters, and power allocation in active RIS-assisted TN-NTN networks.

Findings

Active RIS outperforms passive RIS in energy efficiency.

The proposed H-PPO algorithm enhances network sum rate.

Active RIS reduces outage probability significantly.

Abstract

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate.