Covert Beamforming Design for Intelligent Reflecting Surface Assisted IoT Networks

TL;DR

This paper proposes novel covert beamforming strategies for IRS-assisted IoT networks to enhance secure communication, considering perfect and imperfect knowledge of Willie's channel, and demonstrates their effectiveness through numerical analysis.

Contribution

It introduces joint beamforming designs for IRS-assisted covert communication under both perfect and imperfect channel knowledge scenarios, including robust optimization methods.

Findings

The proposed covert beamformer achieves higher covert rates.

Robust beamforming effectively mitigates channel uncertainties.

Numerical results validate the superiority of the proposed methods.

Abstract

In this paper, we consider covert beamforming design for intelligent reflecting surface (IRS) assisted Internet of Things (IoT) networks, where Alice utilizes IRS to covertly transmit a message to Bob without being recognized by Willie. We investigate the joint beamformer design of Alice and IRS to maximize the covert rate of Bob when the knowledge about Willie's channel state information (WCSI) is perfect and imperfect at Alice, respectively. For the former case, we develop a covert beamformer under the perfect covert constraint by applying semidefinite relaxation. For the later case, the optimal decision threshold of Willie is derived, and we analyze the false alarm and the missed detection probabilities. Furthermore, we utilize the property of Kullback-Leibler divergence to develop the robust beamformer based on a relaxation, S-Lemma and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.