Temperature-Resilient LC-RIS Phase-Shift Design for Multi-user Downlink Communications

TL;DR

This paper proposes a temperature-resilient phase shift design for Liquid Crystal-based RISs in multi-user mmWave downlink communications, addressing temperature sensitivity issues to improve performance and reduce costs.

Contribution

It introduces a novel max-min SNR optimization algorithm that accounts for temperature effects in LC-RIS phase shifts, enhancing robustness in varying climates.

Findings

Significant performance improvement over baseline methods

Effective mitigation of temperature-induced phase shift variations

Demonstrated robustness in diverse temperature conditions

Abstract

The reflecting antenna elements in most reconfigurable intelligent surfaces (RISs) use semiconductor-based (e.g., positive-intrinsic-negative (PIN) diodes and varactors) phase shifters. Although effective, a drawback of this technology is the high power consumption and cost, which become particularly prohibitive in millimeter-wave (mmWave)/sub-Terahertz range. With the advances in Liquid Crystals (LCs) in microwave engineering, we have observed a new trend in using LC for realizing phase shifter networks of RISs. LC-RISs are expected to significantly reduce the fabrication costs and power consumption. However, the nematic LC molecules are sensitive to temperature variations. Therefore, implementing LC-RIS in geographical regions with varying temperatures requires temperature-resilient designs. The mentioned temperature variation issue becomes more significant at higher temperatures as the phase shifter range reduces in warmer conditions, whereas it expands in cooler ones. In this paper, we study the impact of temperature on the operation of LC-RISs and develop a temperature-resilient phase shift design. Specifically, we formulate a max-min signal-to-interference-plus-noise ratio optimization for a multi-user downlink mmWave network that accounts for the impact of temperature in the LC-RIS phase shifts. The simulation results demonstrate a significant improvement for the considered set of parameters when using our algorithm compared to the baseline approach, which neglects the temperature effects.