Cooperative Multistatic Target Detection in Cell-Free Communication Networks

TL;DR

This paper introduces a cooperative sensing method for multistatic target detection in cell-free MIMO networks, leveraging OFDM signals and Bayesian learning to improve detection accuracy and provide theoretical performance insights.

Contribution

It proposes a novel SBL-based cooperative sensing approach for cell-free MIMO networks and offers a theoretical PEP analysis for system design insights.

Findings

Promising detection performance demonstrated through numerical results.

Theoretical PEP analysis provides design insights for illumination and beam patterns.

Effective data fusion from multiple RUs enhances target localization accuracy.

Abstract

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.