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Feasibility Study Regarding Self-sustainable Reconfigurable Intelligent Surfaces

Zhenyu Li, Ozan Alp Topal, Özlem Tuğfe Demir, Emil Björnson, Cicek Cavdar

TL;DR

The paper addresses the feasibility of self-sustainable reconfigurable intelligent surfaces (ssRIS) that harvest energy from incident waves to power operation, eliminating cabling costs. It analyzes two harvest-and-reflect schemes, element splitting (ES) and time splitting (TS), deriving SNR and data-rate expressions for LOS and NLOS ssRIS–UE channels and formulating self-sustainability constraints. Through optimization problems, it quantifies how the minimum required number of elements scales with harvesting conditions, data-rate targets, and outage tolerances, revealing that TS offers stronger channel hardening but exponential growth with harvesting difficulty and rate, while ES shows linear growth and better feasibility in harsh outdoor scenarios. The results guide deployment decisions, indicating TS for indoor, reliable applications and ES for outdoor, robust operation, with practical implications for designing cost-effective ssRIS-enabled networks.

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.

Feasibility Study Regarding Self-sustainable Reconfigurable Intelligent Surfaces

TL;DR

The paper addresses the feasibility of self-sustainable reconfigurable intelligent surfaces (ssRIS) that harvest energy from incident waves to power operation, eliminating cabling costs. It analyzes two harvest-and-reflect schemes, element splitting (ES) and time splitting (TS), deriving SNR and data-rate expressions for LOS and NLOS ssRIS–UE channels and formulating self-sustainability constraints. Through optimization problems, it quantifies how the minimum required number of elements scales with harvesting conditions, data-rate targets, and outage tolerances, revealing that TS offers stronger channel hardening but exponential growth with harvesting difficulty and rate, while ES shows linear growth and better feasibility in harsh outdoor scenarios. The results guide deployment decisions, indicating TS for indoor, reliable applications and ES for outdoor, robust operation, with practical implications for designing cost-effective ssRIS-enabled networks.

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.
Paper Structure (13 sections, 24 equations, 3 figures)

This paper contains 13 sections, 24 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. System design and data rate analysis
  4. ssRIS phase-shifts design
  5. Data rate under different channel conditions
  6. LOS ssRIS-UE channel
  7. NLOS ssRIS-UE channel
  8. Self-sustainability constraints under various HaR schemes
  9. Element splitting
  10. Time splitting
  11. ssRIS-Assisted System Optimization
  12. Numerical Analysis
  13. Conclusion

Figures (3)

  • Figure 1: System model. The square and circle represent the position of the BS and the UE, respectively.
  • Figure 2: Element requirement across different system parameters. LOS and NLOS refer to the propagation conditions of the ssRIS-UE channel. (a) Impact of harvesting condition (via changing transmit power $P$) with $R_0 = 10$ Mbps; (b) Impact of data rate $R_0$ with $P = 0.1$ W.
  • Figure 3: Element requirement across different outage margins under the NLOS ssRIS-UE channel. (a) $P=0.1$ W and $R_0 = 20$ Mbps; (b) $P=0.1$ W and $R_0=15$ Mbps.