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

arXiv: https://arxiv.org/abs/2208.07592 · HTML: https://mlsanity.com/paper/2208.07592/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing](https://mlsanity.com/paper/2102.12780/) (2021)
- [Cram'er-Rao Bound Optimization for Joint Radar-Communication Design](https://mlsanity.com/paper/2101.12530/) (2021)
- [Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2110.02857/) (2021)
- [Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model](https://mlsanity.com/paper/2104.10378/) (2021)
- [Heterogeneous Multi-sensor Fusion with Random Finite Set Multi-object Densities](https://mlsanity.com/paper/2106.08088/) (2021)

## Abstract (arXiv)

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.