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Network-Level Analysis of Integrated Sensing and Communication Using Stochastic Geometry

Ruibo Wang, Baha Eddine Youcef Belmekki, Xue Zhang, Mohamed-Slim Alouini

TL;DR

This paper addresses network-level analysis for Integrated Sensing and Communication (ISAC) by leveraging Stochastic Geometry (SG) to model interference, topology, and channel effects. It establishes a framework that maps ISAC components to SG tools, discusses suitable spatial and channel models, and defines ISAC-specific network types and metrics, including SINR, sensing probabilities, and joint efficiency metrics. A case study of a BS-UAV network with Resident Distribution Inspired (RPDI) and blockage-aware channels demonstrates how topology and fading awareness shape both communication and sensing performance, and shows how UAV deployment parameters can be optimized to enhance overall ISAC performance. The work highlights open issues—model realism, richer joint metrics, and extensions to satellite networks—emphasizing practical pathways for scalable, spectrum-efficient ISAC systems.

Abstract

To meet the demands of densely deploying communication and sensing devices in the next generation of wireless networks, integrated sensing and communication (ISAC) technology is employed to alleviate spectrum scarcity, while stochastic geometry (SG) serves as a tool for low-complexity performance evaluation. To assess network-level performance, there is a natural interaction between ISAC technology and the SG method. From ISAC network perspective, we illustrate how to leverage SG analytical framework to evaluate ISAC network performance by introducing point process distributions and stochastic fading channel models. From SG framework perspective, we summarize the unique performance metrics and research objectives of ISAC networks, thereby extending the scope of SG research in the field of wireless communications. Additionally, considering the limited discussion in the existing SG-based ISAC works in terms of distribution and channel modeling, a case study is designed to exploit topology and channel fading awareness to provide relevant network insights.

Network-Level Analysis of Integrated Sensing and Communication Using Stochastic Geometry

TL;DR

This paper addresses network-level analysis for Integrated Sensing and Communication (ISAC) by leveraging Stochastic Geometry (SG) to model interference, topology, and channel effects. It establishes a framework that maps ISAC components to SG tools, discusses suitable spatial and channel models, and defines ISAC-specific network types and metrics, including SINR, sensing probabilities, and joint efficiency metrics. A case study of a BS-UAV network with Resident Distribution Inspired (RPDI) and blockage-aware channels demonstrates how topology and fading awareness shape both communication and sensing performance, and shows how UAV deployment parameters can be optimized to enhance overall ISAC performance. The work highlights open issues—model realism, richer joint metrics, and extensions to satellite networks—emphasizing practical pathways for scalable, spectrum-efficient ISAC systems.

Abstract

To meet the demands of densely deploying communication and sensing devices in the next generation of wireless networks, integrated sensing and communication (ISAC) technology is employed to alleviate spectrum scarcity, while stochastic geometry (SG) serves as a tool for low-complexity performance evaluation. To assess network-level performance, there is a natural interaction between ISAC technology and the SG method. From ISAC network perspective, we illustrate how to leverage SG analytical framework to evaluate ISAC network performance by introducing point process distributions and stochastic fading channel models. From SG framework perspective, we summarize the unique performance metrics and research objectives of ISAC networks, thereby extending the scope of SG research in the field of wireless communications. Additionally, considering the limited discussion in the existing SG-based ISAC works in terms of distribution and channel modeling, a case study is designed to exploit topology and channel fading awareness to provide relevant network insights.
Paper Structure (31 sections, 5 figures, 1 table)

This paper contains 31 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. On the Use of SG Modeling into ISAC Paradigm
  3. Spatial Distribution
  4. Channel Model
  5. Typical Application Scenarios
  6. Cellular Networks
  7. Vehicular Networks
  8. UAV Networks
  9. Extend ISAC Context Within the SG Framework
  10. Network Type
  11. Communication-Assisted Sensing Networks
  12. Sensing-Assisted Communication Networks
  13. Joint Sensing and Communication Networks
  14. Performance Metric
  15. Signal-to-Interference-plus-Noise Ratio (SINR)
  16. ...and 16 more sections

Figures (5)

  • Figure 1: ISAC network structure diagram.
  • Figure 2: Influence of different UAV distributions on coverage probability.
  • Figure 3: Influence of different UAV distributions on throughput.
  • Figure 4: Sensing metrics under different UAV distributions.
  • Figure 5: Heat map of average sensing probability.