BS Coordination Optimization in Integrated Sensing and Communication: A Stochastic Geometric View

TL;DR

The paper tackles spectrum scarcity and inter-cell interference in large-scale ISAC networks by adopting a stochastic-geometry framework with PPPs for BSs, users, and targets. It introduces cooperative interference nulling via coordinated beamforming with cluster sizes $L$ (communication) and $Q$ (sensing), and jointly optimizes the numbers of served users $K$ and targets $J$. The key contributions are tractable expressions for the network ASEs $T^{ m ASE}_c$ and $T^{ m ASE}_s$, a geometry-based radar information-rate model, and an optimization strategy that reveals a fundamental trade-off: maximizing $T^{ m ASE}_c$ favors full spatial multiplexing while sensing benefits from partial interference nulling. The results demonstrate substantial network gains over time-sharing benchmarks and offer practical guidelines for deploying large-scale ISAC networks with cooperative beamforming.

Abstract

In this study, we explore integrated sensing and communication (ISAC) networks to strike a more effective balance between sensing and communication (S&C) performance at the network scale. We leverage stochastic geometry to analyze the S&C performance, shedding light on critical cooperative dependencies of ISAC networks. According to the derived expressions of network performance, we optimize the user/target loads and the cooperative base station cluster sizes for S&C to achieve a flexible trade-off between network-scale S&C performance. It is observed that the optimal strategy emphasizes the full utilization of spatial resources to enhance multiplexing and diversity gain when maximizing communication ASE. In contrast, for sensing objectives, parts of spatial resources are allocated to cancel inter-cell sensing interference to maximize sensing ASE. Simulation results validate that the proposed ISAC scheme realizes a remarkable enhancement in overall S&C network performance.

Figures (7)

• Figure 1: Illustration of cooperative ISAC networks with separate interference nulling for S&C.
• Figure 2: Illustration of sensing interference hole.
• Figure 3: Communication ASE $T^{\rm{ASE}}_c$ and S&C ASE $T^{\rm{ASE}}_{\rm{sum}}$ with respect to $K$.
• Figure 4: Optimal cooperative cluster size $L$ of the maximized communication ASE.
• Figure 5: Sensing ASE $T^{\rm{ASE}}_s$ and S&C ASE $T^{\rm{ASE}}_{\rm{sum}}$ comparisons versus cooperative cluster size $Q$.