Performance of UAV-based Cell-free mMIMO ISAC Networks: Tethered vs. Mobile

TL;DR

This paper evaluates the sensing performance of UAV-based cell-free mMIMO ISAC networks, comparing tethered and mobile UAV deployments, with mobile UAVs optimized via PSO to enhance sensing SINR.

Contribution

It introduces a joint precoding and UAV position optimization framework for mobile UAVs in cell-free mMIMO ISAC networks, demonstrating improved sensing SINR over tethered UAVs.

Findings

Mobile UAVs with position optimization outperform tethered UAVs in sensing SINR.

Tethered UAVs have more efficient power allocation.

Proposed algorithms significantly enhance sensing performance.

Abstract

The employment of unmanned aerial vehicles (UAVs) aligned with multistatic sensing in integrated sensing and communication (ISAC) systems can provide remarkable performance gains in sensing, by taking advantage of the cell-free massive multiple-input multiple-output (mMIMO) architecture. Under these considerations, in this paper, the achievable sensing signal-to-noise-plus-interference ratio (SINR) of a cell-free mMIMO ISAC UAV-based network is evaluated for two different deployments of UAVs, namely, mobile and tethered. In both scenarios, a transmit precoder that jointly optimizes the sensing and communication requirements subjected to power constraints is designed. Specifically, for the scenario with mobile UAVs, beyond the transmit precoding, we also optimize the position of the transmit UAVs through particle swarm optimization (PSO). The results show that, although tethered UAVs have a more efficient power allocation, the proposed position control algorithm for the mobile UAVs can achieve a superior gain in terms of sensing SINR.