On the Position Optimization of IRS

Zhu, Jianyue and Huang, Yongming and Wang, Jiaheng and Navaie, Keivan and Ding, Zhiguo (2022) On the Position Optimization of IRS. IEEE Internet of Things Journal, 9 (14). 11712 - 11724. ISSN 2327-4662

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Abstract

The intelligent reflecting surface (IRS) technology is emerged as an enabling technology for beyond 5G systems and IoT networks in which the signal propagation is reconfigured to enhance wireless system performance. IRS consists of many passive elements and each reflecting the incident signal with a certain phase shift to collectively achieve the required beamforming. The IRS is to be a low profile and lightweight setting with a conformal geometry hence its position can be easily engineered to achieve certain performance enhancements. In the current literature, however, the flexibility in the IRS position is often overlooked since it is considered as a given fixture. We argue that optimizing the IRS position provides a new degree of freedom in the network design and enables extra performance gain. In this paper, we analytically characterize the optimal IRS s position to maximize the achievable system rate. We then obtain the optimal IRS positions for different IRS settings with fixed-height and variable-height and consider both cost-efficient equal phase shift IRS, and non-equal phase shift IRS that enables sophisticated beamforming. We further incorporate antenna directivity in our analysis and investigate its effect on the optimal IRS position in each case. Simulation results show that the provided optimal position yields higher performance than settings with random IRS locations. Our results provide significant practical insights on the network coverage design using the IRS.