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System-level Analysis of Dual-Mode Networked Sensing: ISAC Integration & Coordination Gains

Yasser Nabil, Hesham ElSawy, Hossam S. Hassanein

TL;DR

The paper tackles the challenge of jointly enabling sensing and communication in dense mmWave networks by proposing a dual-mode networked sensing framework that combines monostatic and multistatic operations. It develops a large-scale stochastic-geometry model with unified ISAC waveforms, comprehensive interference and clutter modeling, and SIC considerations to quantify integration and coordination gains. Through mutual-information-based sensing metrics and LT-based analysis, it demonstrates significant performance improvements from spatial diversity and cooperative sensing, and identifies a SIC-driven mode transition between monostatic and multistatic dominance. The work provides actionable design guidelines on beamwidth, BS density, power allocation, and cooperative cluster sizing, showing that ISAC can deliver substantial throughput gains without requiring full FD capability. These insights advance practical deployment strategies for next-generation cellular networks that simultaneously support high-rate communication and precise sensing.

Abstract

This paper characterizes integration and coordination gains in dense millimeter-wave ISAC networks through a dual-mode framework that combines monostatic and multistatic sensing. A comprehensive system-level analysis is conducted, accounting for base station (BS) density, power allocation, antenna misalignment, radar cross-section (RCS) fluctuations, clutter, bistatic geometry, channel fading, and self-interference cancellation (SIC) efficiency. Using stochastic geometry, coverage probabilities and ergodic rates for sensing and communication are derived, revealing tradeoffs among BS density, beamwidth, and power allocation. It is shown that the communication performance sustained reliable operation despite the overlaid sensing functionality. In contrast, the results reveal the foundational role of spatial sensing diversity, driven by the dual-mode operation, to compensate for the weak sensing reflections and vulnerability to imperfect SIC along with interference and clutter. To this end, we identify a system transition from monostatic to multistatic-dominant sensing operation as a function of the SIC efficiency. In the latter case, using six multistatic BSs instead of a single bistatic receiver improved sensing coverage probability by over 100%, highlighting the coordination gain. Moreover, comparisons with pure communication networks confirm substantial integration gain. Specifically, dual-mode networked sensing with four cooperative BSs can double throughput, while multistatic sensing alone improves throughput by over 50%.

System-level Analysis of Dual-Mode Networked Sensing: ISAC Integration & Coordination Gains

TL;DR

The paper tackles the challenge of jointly enabling sensing and communication in dense mmWave networks by proposing a dual-mode networked sensing framework that combines monostatic and multistatic operations. It develops a large-scale stochastic-geometry model with unified ISAC waveforms, comprehensive interference and clutter modeling, and SIC considerations to quantify integration and coordination gains. Through mutual-information-based sensing metrics and LT-based analysis, it demonstrates significant performance improvements from spatial diversity and cooperative sensing, and identifies a SIC-driven mode transition between monostatic and multistatic dominance. The work provides actionable design guidelines on beamwidth, BS density, power allocation, and cooperative cluster sizing, showing that ISAC can deliver substantial throughput gains without requiring full FD capability. These insights advance practical deployment strategies for next-generation cellular networks that simultaneously support high-rate communication and precise sensing.

Abstract

This paper characterizes integration and coordination gains in dense millimeter-wave ISAC networks through a dual-mode framework that combines monostatic and multistatic sensing. A comprehensive system-level analysis is conducted, accounting for base station (BS) density, power allocation, antenna misalignment, radar cross-section (RCS) fluctuations, clutter, bistatic geometry, channel fading, and self-interference cancellation (SIC) efficiency. Using stochastic geometry, coverage probabilities and ergodic rates for sensing and communication are derived, revealing tradeoffs among BS density, beamwidth, and power allocation. It is shown that the communication performance sustained reliable operation despite the overlaid sensing functionality. In contrast, the results reveal the foundational role of spatial sensing diversity, driven by the dual-mode operation, to compensate for the weak sensing reflections and vulnerability to imperfect SIC along with interference and clutter. To this end, we identify a system transition from monostatic to multistatic-dominant sensing operation as a function of the SIC efficiency. In the latter case, using six multistatic BSs instead of a single bistatic receiver improved sensing coverage probability by over 100%, highlighting the coordination gain. Moreover, comparisons with pure communication networks confirm substantial integration gain. Specifically, dual-mode networked sensing with four cooperative BSs can double throughput, while multistatic sensing alone improves throughput by over 50%.

Paper Structure

This paper contains 25 sections, 12 theorems, 51 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. ISAC Network and Signal Models
  4. Beamforming Models
  5. Channel and Propagation Models
  6. Types of Interference and Distance Distributions
  7. Monostatic Sensing Analysis
  8. Bistatic Sensing Analysis
  9. The Cooperative Networked Sensing
  10. Sensing information Rate
  11. Communication Analysis
  12. SINR Formulation
  13. The Impact of Misalignment Error
  14. Communication Coverage Probability
  15. Communication Rate
  16. ...and 10 more sections

Key Result

Lemma 1

The LT of the aggregate direct interference seen by the typical BS located at the origin is given in (dir_int), where $\bold p_{\bold{LOS}}\left(r\right)$ is given by (los_prop).

Figures (6)

  • Figure 1: An illustration of the system model with a sensing cluster of four cooperative BSs per target. Each BS forms $M$ beams on $M$ distinct frequency blocks reused across all BSs, with each beam simultaneously sensing a target and serving a MU. For visual clarity, only one beam per BS is shown, and the depicted beams share the same frequency block. The monostatic distance is $R_1$, while each $(R_1, R_n)$ pair forms a bistatic setup, where $R_n$ is the distance between the target and its $n^\text{th}$-nearest BS ($n \geq 2$).
  • Figure 2: An illustration for the unified ISAC signal.
  • Figure 3: Interference sources in monostatic operation (left) and bistatic operation (right), the bistatic baseline between the TX and RX BSs is indicated by $L_n$, and $\beta$ is the bistatic angle. Each interference type is shown with a single representative signal for clarity.
  • Figure 4: (a) The effect of different interference types on the monostatic and the bistatic ($n=2$) operations. (b) The sensing coverage probability for different target locations. (c) The average networked sensing coverage probability with $N=4$ against the scale parameter $\sigma_{\text{av}_{cl}}$ of the Weibull clutter distribution for shape parameters $k \in \{0.7, 1, 1.5\}$.
  • Figure 5: (a) The average coverage probability versus beamwidth spread. (b) The average sensing and communication rates versus BS density. (c) Average sensing coverage probability (left $y$-axis) and the payload backhaul overhead per target (right $y$-axis) versus the number of cooperating BSs.
  • ...and 1 more figures

Theorems & Definitions (17)

  • Remark 1
  • Remark 2
  • Remark 3
  • Lemma 1
  • Lemma 2
  • Lemma 3
  • Theorem 1
  • Lemma 4
  • Lemma 5
  • Theorem 2
  • ...and 7 more