Feasibility Study Regarding Self-sustainable Reconfigurable Intelligent Surfaces

TL;DR

This paper evaluates the feasibility of self-sustainable reconfigurable intelligent surfaces (ssRISs) using two harvest-and-reflect schemes, analyzing their deployment potential and operational requirements in various wireless communication scenarios.

Contribution

It introduces and compares two novel ssRIS schemes, element-splitting and time-splitting, providing analytical insights into their feasibility under different environmental and system conditions.

Findings

TS scheme offers stable element requirements across outage margins.

ES scheme scales linearly with harvesting difficulty, better for outdoor scenarios.

TS is advantageous for indoor, reliable deployments.

Abstract

Without requiring operational costs such as cabling and powering while maintaining reconfigurable phase-shift capability, self-sustainable reconfigurable intelligent surfaces (ssRISs) can be deployed in locations inaccessible to conventional relays or base stations, offering a novel approach to enhance wireless coverage. This study assesses the feasibility of ssRIS deployment by analyzing two harvest-and-reflect (HaR) schemes: element-splitting (ES) and time-splitting (TS). We examine how element requirements scale with key system parameters, transmit power, data rate demands, and outage constraints under both line-of-sight (LOS) and non-line-of-sight (NLOS) ssRIS-to-user equipment (UE) channels. Analytical and numerical results reveal distinct feasibility characteristics. The TS scheme demonstrates better channel hardening gain, maintaining stable element requirements across varying outage margins, making it advantageous for indoor deployments with favorable harvesting conditions and moderate data rates. However, TS exhibits an element requirement that exponentially scales to harvesting difficulty and data rate. Conversely, the ES scheme shows only linear growth with harvesting difficulty, providing better feasibility under challenging outdoor scenarios. These findings establish that TS excels in benign environments, prioritizing reliability, while ES is preferable for demanding conditions requiring operational robustness.