Joint Active and Passive Beamforming in NOMA Based UAV-IRS Assisted Covert Communication

Abstract

Information security remains a paramount concern in wireless networks. Covert communication offers a unique solution by hiding the transmission, making it undetectable by the warden (Willie). This thesis investigates the approach for maximizing covert data rate in a scenario where a multi-antenna transmitter (Alice) transmits to a covert user (Bob) and a public user (Roy) through a UAV-IRS assisted relay, leveraging Non Orthogonal Multiple Access (NOMA). The public user served by NOMA acts as artificial noise, masking the covert transmission from a warden (Willie). The system leverages Unmanned Aerial Vehicles (UAVs) for flexible deployment and Intelligent Reflecting Surfaces (IRSs) to manipulate signal propagation. Deep Deterministic Policy Gradient-A Deep Reinforcement Learning (DRL) approach is employed to optimize both transmit beamforming at the transmitter and phase shifts at UAV-IRS, maximizing the covert data rate at Bob, ensuring at least a minimum data rate at Roy while minimizing signal strength at the warden. This UAV-IRS assisted NOMA with the DRL approach offers a promising solution for secure and reliable covert communication in various scenarios.