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Cooperative ISAC Networks: Performance Analysis, Scaling Laws and Optimization

Kaitao Meng, Christos Masouros, Athina P. Petropulu, Lajos Hanzo

TL;DR

This work tackles how to balance sensing and communication in large-scale ISAC networks by leveraging cooperative, CoMP-enabled transmission and distributed radar. It formulates a network model with target-centric sensing and user-centric communication clusters under backhaul limits, analyzed via stochastic geometry to yield tractable S&C performance expressions. The authors discover a ln^2 N scaling for the average cooperative sensing CRLB with increasing sensing BSs, provide closed-form rate expressions and an approximated optimal cluster-size design, and formulate a rate-CRLB region and a weighted ISAC objective to study tradeoffs. Simulations confirm significant network-wide gains from cooperation over time-sharing schemes, offering practical guidelines for scalable ISAC deployment and cluster optimization.

Abstract

Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, the potent tool of stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the Cramer-Rao lower bound (CRLB) expression of the localization accuracy derived unveils a significant finding: Deploying N ISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the ln^2N scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of N^2 achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance.

Cooperative ISAC Networks: Performance Analysis, Scaling Laws and Optimization

TL;DR

This work tackles how to balance sensing and communication in large-scale ISAC networks by leveraging cooperative, CoMP-enabled transmission and distributed radar. It formulates a network model with target-centric sensing and user-centric communication clusters under backhaul limits, analyzed via stochastic geometry to yield tractable S&C performance expressions. The authors discover a ln^2 N scaling for the average cooperative sensing CRLB with increasing sensing BSs, provide closed-form rate expressions and an approximated optimal cluster-size design, and formulate a rate-CRLB region and a weighted ISAC objective to study tradeoffs. Simulations confirm significant network-wide gains from cooperation over time-sharing schemes, offering practical guidelines for scalable ISAC deployment and cluster optimization.

Abstract

Integrated sensing and communication (ISAC) networks are investigated with the objective of effectively balancing the sensing and communication (S&C) performance at the network level. Through the simultaneous utilization of multi-point (CoMP) coordinated joint transmission and distributed multiple-input multiple-output (MIMO) radar techniques, we propose an innovative networked ISAC scheme, where multiple transceivers are employed for collaboratively enhancing the S&C services. Then, the potent tool of stochastic geometry is exploited for characterizing the S&C performance, which allows us to illuminate the key cooperative dependencies in the ISAC network and optimize salient network-level parameters. Remarkably, the Cramer-Rao lower bound (CRLB) expression of the localization accuracy derived unveils a significant finding: Deploying N ISAC transceivers yields an enhanced average cooperative sensing performance across the entire network, in accordance with the ln^2N scaling law. Crucially, this scaling law is less pronounced in comparison to the performance enhancement of N^2 achieved when the transceivers are equidistant from the target, which is primarily due to the substantial path loss from the distant base stations (BSs) and leads to reduced contributions to sensing performance gain. Moreover, we derive a tight expression of the communication rate, and present a low-complexity algorithm to determine the optimal cooperative cluster size. Based on our expression derived for the S&C performance, we formulate the optimization problem of maximizing the network performance in terms of two joint S&C metrics. To this end, we jointly optimize the cooperative BS cluster sizes and the transmit power to strike a flexible tradeoff between the S&C performance.
Paper Structure (19 sections, 53 equations, 10 figures)

This paper contains 19 sections, 53 equations, 10 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Cooperative ISAC Network Model
  4. Cooperative Sensing Model
  5. Cooperative Communication Model
  6. Limited Backhaul Capacity Model
  7. Sensing Performance Analysis
  8. Geometry Gain of Cooperative Sensing
  9. Performance Gain of Cooperative Sensing
  10. Cooperation Acceptance Probability for Limited Resource Blocks
  11. Communication Performance Analysis
  12. Expression of Communication Rate
  13. Optimal Cooperative Cluster Size
  14. Tradeoff Between Sensing and Communication
  15. Simulation Results
  16. ...and 4 more sections

Figures (10)

  • Figure 1: Illustration of cooperative S&C networks.
  • Figure 2: Illustration of the S-C network-level performance region.
  • Figure 3: GDoP value vs. the number $N$ of cooperative BSs.
  • Figure 4: Root CRLB value vs. the number $N$ of cooperative BSs when the configuration of the acceptance probability is 1.
  • Figure 5: Root CRLB value vs. the number $N$ of cooperative BSs for the cooperation acceptance probability derived in (\ref{['AcceptationSensing']}).
  • ...and 5 more figures