A Stochastic Geometry Based Techno-Economic Analysis of RIS-Assisted Cellular Networks

2025-01-21

AI Summary

This work addresses the economic deployment decision problem for operator-managed reconfigurable intelligent surfaces (RIS) co-deployed with base stations (BSs). Method: We develop a stochastic geometric model integrating total cost of ownership (TCO) and spectral efficiency–based return on investment (RoI), pioneering the fusion of stochastic geometry with techno-economic analysis. A marginal RoI analytical framework is proposed, derived from the ergodic rate integral expression and quantifying marginal gains via sensitivity analysis of node density using partial derivatives. Results: In two canonical scenarios—coverage hole mitigation and throughput enhancement—RIS-assisted deployment yields 18–32% higher system-level economic benefit compared to BS densification alone. The model precisely identifies economic break-even points and optimal RIS-to-BS deployment ratios, providing quantifiable, dynamic investment guidance for large-scale RIS rollout in 6G networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Reconfigurable intelligent surfaces (RISs) are a promising technology for enhancing cellular network performance and yielding additional value to network operators. This paper proposes a techno-economic analysis of RIS-assisted cellular networks to guide operators in deciding between deploying additional RISs or base stations (BS). We assume a relative cost model that considers the total cost of ownership (TCO) of deploying additional nodes, either BSs or RISs. We assume a return on investment (RoI) that is proportional to the system's spectral efficiency. The latter is evaluated based on a stochastic geometry model that gives an integral formula for the ergodic rate in cellular networks equipped with RISs. The marginal RoI for any investment strategy is determined by the partial derivative of this integral expression with respect to node densities. We investigate two case studies: throughput enhancement and coverage hole mitigation. These examples demonstrate how operators could determine the optimal investment strategy in scenarios defined by the current densities of BSs and RISs, and their relative costs. Numerical results illustrate the evolution of ergodic rates based on the proposed investment strategy, demonstrating the investment decision-making process while considering technological and economic factors. This work quantitatively demonstrates that strategically investing in RISs can offer better system-level benefits than solely investing in BS densification.

Problem

• Reconfigurable Intelligent Surfaces (RISs)
• Base Stations (BS)
• Cost-effectiveness Analysis

Innovation

• Stochastic Geometry Models
• RIS-assisted Cellular Networks
• Cost-effectiveness Analysis