DRL-Based Resource Allocation for Energy-Efficient IRS-Assisted UAV Spectrum Sharing Systems

TL;DR

This paper proposes a DRL-based method to optimize energy efficiency in IRS-assisted UAV spectrum sharing systems with OFDM, jointly optimizing beamforming, subcarrier allocation, IRS phase shifts, and UAV trajectory.

Contribution

It introduces a novel joint optimization framework for IRS-assisted UAV systems and develops a DRL approach to solve complex, non-convex problems effectively.

Findings

Significant energy efficiency improvements over benchmarks

Effective joint optimization of beamforming, IRS phase shifts, and UAV trajectory

Robustness demonstrated through extensive experiments

Abstract

Intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) systems provide a new paradigm for reconfigurable and flexible wireless communications. To enable more energy efficient and spectrum efficient IRS assisted UAV wireless communications, this paper introduces a novel IRS-assisted UAV enabled spectrum sharing system with orthogonal frequency division multiplexing (OFDM). The goal is to maximize the energy efficiency (EE) of the secondary network by jointly optimizing the beamforming, subcarrier allocation, IRS phase shifts, and the UAV trajectory subject to practical transmit power and passive reflection constraints as well as UAV physical limitations. A physically grounded propulsion-energy model is adopted, with its tight upper bound used to form a tractable EE lower bound for the spectrum sharing system. To handle highly non convex, time coupled optimization problems with a mixed continuous and discrete policy space, we develop a deep reinforcement learning (DRL) approach based on the actor critic framework. Extended experiments show the significant EE improvement of the proposed DRL-based approach compared to several benchmark schemes, thus demonstrating the effectiveness and robustness of the proposed approach with mobility.