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

Guodong Sun, Francois Baccelli, Luis Uzeda Garcia, Stefano Paris

TL;DR

The paper tackles the problem of deciding between deploying additional base stations or RIS-based infrastructure in cellular networks by marrying stochastic geometry with a techno-economic framework. It develops integral expressions for the mean ergodic rate of a typical user, and provides first-order derivatives of this rate with respect to BS and RIS densities to quantify marginal RoI. Two case studies—throughput enhancement and coverage hole mitigation—are analyzed, with an incremental investment policy that weighs performance gains against total deployment costs. Numerical results demonstrate scenarios where RIS deployment, especially with larger RIS panels, can outperform densification alone, offering practical guidance for operators seeking to maximize ROI in RIS-assisted networks.

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.

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

TL;DR

The paper tackles the problem of deciding between deploying additional base stations or RIS-based infrastructure in cellular networks by marrying stochastic geometry with a techno-economic framework. It develops integral expressions for the mean ergodic rate of a typical user, and provides first-order derivatives of this rate with respect to BS and RIS densities to quantify marginal RoI. Two case studies—throughput enhancement and coverage hole mitigation—are analyzed, with an incremental investment policy that weighs performance gains against total deployment costs. Numerical results demonstrate scenarios where RIS deployment, especially with larger RIS panels, can outperform densification alone, offering practical guidance for operators seeking to maximize ROI in RIS-assisted networks.

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.
Paper Structure (19 sections, 5 theorems, 58 equations, 7 figures, 1 algorithm)

This paper contains 19 sections, 5 theorems, 58 equations, 7 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Stochastic Geometry System Model
  3. Thoughput Enhancement Model
  4. Coverage Hole Mitigation Model
  5. System Performance Analysis
  6. Total Cost of Ownership
  7. Sensitivity Analysis and Investment Strategy
  8. Integral Expression for Mean Ergodic Rate
  9. Sensitivity Analysis for Throughput Enhancement
  10. Sensitivity Analysis for Coverage Hole Mitigation
  11. Incremental Investment Strategy
  12. Numerical Results and Decision Analysis
  13. Performance of RISs for Throughput Enhancement
  14. Evaluating the Impact of Coverage Hole Mitigation
  15. Conclusion
  16. ...and 4 more sections

Key Result

Theorem 1

The coverage probability for a typical UE located at a distance of $r$ to the associated BS is given by where $\mathcal{B}_{\Upsilon^+}(s)$ is given by the formula Here, $\int_{-\infty}^{\infty}\frac{{\rm d}u}{u}$ is understood in the sense of Cauchy principal-value, that is $\int_{-\infty}^{\infty}= \lim_{\epsilon \downarrow 0^+}\int_{-\infty}^{-\epsilon}+ \int_{\epsilon}^{\infty}$.

Figures (7)

  • Figure 1: Modeling RIS-assisted Cellular Networks with Matérn Cluster Processes, with a guard zone to prevent the typical UE from being near BSs or RISs.
  • Figure 2: RISs are deployed to assist UEs in coverage holes
  • Figure 3: Mean ergodic rate of a typical UE as a function of the density of BSs for different RIS densities.
  • Figure 4: Ergodic rate evolution based on investment in either BSs or RISs using Algorithm \ref{['alg:strategy']}
  • Figure 5: Should one deploy more BSs before deploying RISs?
  • ...and 2 more figures

Theorems & Definitions (18)

  • Theorem 1
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Theorem 2
  • Remark 1
  • proof
  • proof
  • Lemma 3
  • ...and 8 more