Scaling Law Tradeoff Between Throughput and Sensing Distance in Large ISAC Networks

2025-02-14

AI Summary

This work investigates the fundamental trade-off between communication throughput and sensing range in large-scale self-organizing integrated sensing and communication (ISAC) wireless networks. Leveraging stochastic geometry modeling and path-loss-dominated channels, we rigorously characterize the optimal scaling law under a power-scaling constraint where transmit power decays with communication distance. Using information-theoretic converse arguments and explicit achievability constructions, we derive an explicit functional relationship between the throughput degradation factor and the achievable sensing-range exponent. Furthermore, we prove that this trade-off remains tight even under random fading channels and cannot be circumvented by arbitrary power or distance scaling. Our results establish the first theoretical capacity benchmark and robustness guarantee for ISAC network design, providing foundational insights into fundamental limits of joint communication-sensing performance in large-scale distributed systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Other Foundations of Search & Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

In this paper, we investigate the fundamental tradeoff between communication and sensing performance of emph{ad hoc} integrated sensing and communication (ISAC) wireless networks. Specifically, we consider that $n$ nodes are randomly located in an extended network with area $n$ and transmit ISAC signals. Under the pure path loss channel gain model and the condition that the transmission power scales according to the communication distance, we fully characterize the optimal scaling law tradeoff between throughput and sensing distance by proposing an achievable scheme and proving its converse. Our results can be interpreted as follows: by reducing the throughput by a factor of a function of $n$, the sensing range order improves according to the same function of $n$, raised to the power of the ratio between the path loss factors in communication and sensing. We prove that the same result also holds true for ISAC networks with random fading, despite the uncertainty on the connectivity and power level created by random fading. In addition, we show that the scaling law tradeoff cannot be improved by allowing the transmission power and communication distance to scale freely. To the best of our knowledge, this is the first work formally formulating and characterizing the communication and sensing performance scaling law tradeoff of emph{ad hoc} ISAC networks.

Problem

• Tradeoff between throughput and sensing distance
• Optimal scaling law in ISAC networks
• Impact of path loss and random fading

Innovation

• Optimal scaling law tradeoff analysis
• Achievable scheme with converse proof
• Tradeoff holds under random fading