Covert Multicast in UAV-Enabled Wireless Communication Systems With One-hop and Two-hop Strategies

TL;DR

This paper investigates covert multicast strategies in UAV-enabled wireless systems, proposing one-hop and two-hop schemes, optimizing system parameters under covertness constraints, and validating the approaches with numerical results.

Contribution

It introduces a theoretical framework and optimization algorithms for covert multicast in UAV systems, addressing both one-hop and two-hop transmission schemes.

Findings

Optimal transmit power and prior probability settings enhance covertness.

Particle swarm optimization effectively solves the joint optimization problem.

Numerical results demonstrate the efficiency of the proposed algorithms.

Abstract

This paper delves into the time-efficient covert multicast in a wireless communication system facilitated by Unmanned Aerial Vehicle (UAV), in which the UAV aims to disseminate a common covert information to multiple ground users (GUs) while suffering from the risk of detection by a ground warden (Willie). We propose one hop (OH) and two hop (TH) transmission schemes, first develop a theoretical framework for performance modeling of both the detection error probability at Willie and the transmission time at UAV. The optimization problems subject to the covertness constraint for the two transmission schemes are then formulated to gain insights into the system settings of the UAV's prior transmit probability, transmit power and horizontal location that affect the minimum transmission time. The optimization problems are non-convex and challenging to give numerical results. We thus explore the optimal setting of the transmit power and the prior transmit probability for the UAV separately under specific parameters with two schemes. We further propose a particle swarm optimization (PSO) based algorithm and an exhaustive algorithm to provide the joint solutions for the optimization problem with the OH transmission scheme and TH scheme, respectively. Finally, the efficiency of the proposed PSO-based algorithm is substantiated through extensive numerical results.