Robust Symbol-Level Precoding and Passive Beamforming for IRS-Aided Communications
Guangyang Zhang, Chao Shen, Bo Ai, Zhangdui Zhong

TL;DR
This paper proposes a robust joint beamforming approach combining symbol-level precoding and passive beamforming in IRS-assisted multiuser MISO systems, effectively reducing power consumption under channel uncertainties.
Contribution
It introduces a novel robust optimization framework for joint beamforming with IRS, employing AO and PGD algorithms for efficient implementation in multiuser scenarios.
Findings
AO-based algorithm has lower complexity with similar performance to SDR-based.
Symbol-level precoding significantly improves system performance.
3-bit phase shifters nearly match the power efficiency of ideal IRS configurations.
Abstract
This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consideration of bounded channel uncertainty. The joint beamforming design is formulated into a nonconvex worst-case robust programming to minimize the transmit power subject to single-to-noise ratio (SNR) requirements. To address the challenges due to the constant modulus and the coupling of the beamformers, we first study the single-user case. Specifically, we propose and compare two algorithms based on the semidefinite relaxation (SDR) and alternating optimization (AO) methods, respectively. It turns out that the AO-based algorithm has much lower computational complexity but…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Underwater Vehicles and Communication Systems · Indoor and Outdoor Localization Technologies
