Covert Beamforming Design for Intelligent-Reflecting-Surface-Assisted IoT Networks
Shuai Ma, Yunqi Zhang, Hang Li, Junchang Sun, Jia Shi, Han Zhang, Chao Shen, Shiyin Li
Abstract
In this article, 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 latter 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 the Kullback–Leibler divergence to develop the robust beamformer based on a relaxation, <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-Lemma, and alternate iteration approach. Finally, the numerical experiments evaluate the performance of the proposed covert beamformer design and robust beamformer design.