A Framework of Robust Transmission Design for IRS-Aided MISO Communications With Imperfect Cascaded Channels

Zhou, Gui, Pan, Cunhua, Ren, Hong, Wang, Kezhi and Nallanathan, Arumugam (2020) A Framework of Robust Transmission Design for IRS-Aided MISO Communications With Imperfect Cascaded Channels. IEEE Transactions on Signal Processing, 68. pp. 5092-5106. ISSN 1053-587X

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Official URL: https://doi.org/10.1109/TSP.2020.3019666


Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the worst-case rate constraints under the bounded CSI error model, and the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the worst-case rate constraints by using the S-procedure and the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Item Type: Article
Uncontrolled Keywords: Intelligent reflecting surface (IRS), reconfigurable intelligent surface (RIS), robust design, imperfect channel state information (CSI), cascaded BS-IRS-user channels
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 01 Dec 2020 10:44
Last Modified: 31 Jul 2021 14:00
URI: http://nrl.northumbria.ac.uk/id/eprint/44876

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