Multi-Point Integrated Sensing and Communication: Fusion Model and Functionality Selection

TL;DR

This paper introduces a multi-point ISAC system that fuses data from multiple devices to enhance sensing accuracy and employs an adaptive functionality selection to balance sensing and communication, demonstrating improved performance through simulations.

Contribution

It proposes a fusion model for multi-point ISAC and an adaptive functionality selection module to optimize sensing and communication trade-offs.

Findings

MPISAC outperforms benchmark schemes in sensing accuracy.

The adaptive module effectively balances sensing and communication functions.

Simulation results confirm the approach's ability to span the ISAC performance trade-off region.

Abstract

Integrated sensing and communication (ISAC) represents a paradigm shift, where previously competing wireless transmissions are jointly designed to operate in harmony via the shared use of the hardware platform for improving the spectral and energy efficiencies. However, due to adversarial factors such as fading and interference, ISAC may suffer from high sensing uncertainties. This paper presents a multi-point ISAC (MPISAC) system that fuses the outputs from multiple ISAC devices for achieving higher sensing performance by exploiting multi-view data redundancy. Furthermore, we propose to effectively explore the performance trade-off between sensing and communication via a functionality selection module that adaptively determines the working state (i.e., sensing or communication) of an ISAC device. The crux of our approach is to derive a fusion model that predicts the fusion accuracy via hypothesis testing and optimal voting analysis. Simulation results demonstrate the superiority of MPISAC over various benchmark schemes and show that the proposed approach can effectively span the trade-off region in ISAC systems.