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RIS-assisted Coverage Enhancement in mmWave Integrated Sensing and Communication Networks

Xu Gan, Chongwen Huang, Zhaohui Yang, Xiaoming Chen, Faouzi Bader, Zhaoyang Zhang, Chau Yuen, Yong Liang Guan, Merouane Debbah

TL;DR

This work addresses the vulnerability of mmWave ISAC networks to blockages by introducing distributed RISs and a stochastic-geometry framework to quantify joint ISAC coverage. It develops a beam-pattern model and two user association policies to capture the coupling between communication and sensing, deriving conditional coverage probabilities for LoS and VLoS associations and obtaining the marginal coverage via distance-dependent thinning. Theoretical results are validated by simulations, showing substantial joint ISAC coverage gains (e.g., rising from 67.1% to 92.2%) with RIS deployment and providing deployment guidelines under blockage-heavy conditions. The approach enables principled design of RIS densities and BS-RIS layouts to balance sensing and communication performance in ISAC networks.

Abstract

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we investigate the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Specifically, taking into account the coupling effect of ISAC dual functions within the same network topology, we derive the conditional coverage probability of ISAC performance for two association cases, based on the proposed beam pattern model and user association policies. Then, the marginal coverage rate is calculated by combining these two cases through the distance-dependent thinning method. Simulation results verify the accuracy of derived theoretical formulations and provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$ BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from $67.1\%$ to $92.2\%$ with the deployment of RISs.

RIS-assisted Coverage Enhancement in mmWave Integrated Sensing and Communication Networks

TL;DR

This work addresses the vulnerability of mmWave ISAC networks to blockages by introducing distributed RISs and a stochastic-geometry framework to quantify joint ISAC coverage. It develops a beam-pattern model and two user association policies to capture the coupling between communication and sensing, deriving conditional coverage probabilities for LoS and VLoS associations and obtaining the marginal coverage via distance-dependent thinning. Theoretical results are validated by simulations, showing substantial joint ISAC coverage gains (e.g., rising from 67.1% to 92.2%) with RIS deployment and providing deployment guidelines under blockage-heavy conditions. The approach enables principled design of RIS densities and BS-RIS layouts to balance sensing and communication performance in ISAC networks.

Abstract

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we investigate the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Specifically, taking into account the coupling effect of ISAC dual functions within the same network topology, we derive the conditional coverage probability of ISAC performance for two association cases, based on the proposed beam pattern model and user association policies. Then, the marginal coverage rate is calculated by combining these two cases through the distance-dependent thinning method. Simulation results verify the accuracy of derived theoretical formulations and provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from to with the deployment of RISs.
Paper Structure (14 sections, 5 theorems, 17 equations, 3 figures)

This paper contains 14 sections, 5 theorems, 17 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. Channel Model
  5. The direct channel
  6. The RIS-assisted channel
  7. Association Policies
  8. Coverage Rate in ISAC Networks
  9. Numerical Results
  10. Conclusion
  11. Proof of Lemma 1
  12. Proof of Lemma 2
  13. Proof of Proposition 2
  14. Proof of Proposition 3

Key Result

Proposition 1

(Distance-Dependent Thinning of homogeneous PPP SG_RIS1) When performing the distance-dependent thinning with probability $1-g(r)$ on a homogeneous PPP $\Phi$ with density $\lambda$, an inhomogeneous PPP $\tilde{\Phi}$ with density $\tilde{\lambda}(r) = g(r) \lambda$ will be obtained.

Figures (3)

  • Figure 1: The PDFs of $d_{0,L}^{B-U}$, $d_{0,V}^{B-U}$ and $\eta_0$, derived in Lemma 1, versus different BS and RIS desenties.
  • Figure 2: The achievable rate trade-off between ISAC performance versus different densities of BSs, RISs and blockages.
  • Figure 3: The joint coverage rate of ISAC performance, derived in Theorem 1, versus different SINR thresholds and RIS densities under $\lambda_L=600$ km${}^{-2}$.

Theorems & Definitions (5)

  • Proposition 1
  • Lemma 1
  • Proposition 2
  • Proposition 3
  • Theorem 1