Cooperative Multistatic Target Detection in Cell-Free Communication Networks

Fuente: arXiv
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Autori principali: Yang, Tianyu, Li, Shuangyang, Song, Yi, Zhi, Kangda, Caire, Giuseppe
Natura: Preprint
Pubblicazione: 2024
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author Yang, Tianyu
Li, Shuangyang
Song, Yi
Zhi, Kangda
Caire, Giuseppe
author_facet Yang, Tianyu
Li, Shuangyang
Song, Yi
Zhi, Kangda
Caire, Giuseppe
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2410_16140
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Cooperative Multistatic Target Detection in Cell-Free Communication Networks
Yang, Tianyu
Li, Shuangyang
Song, Yi
Zhi, Kangda
Caire, Giuseppe
Information Theory
Signal Processing
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.
title Cooperative Multistatic Target Detection in Cell-Free Communication Networks
topic Information Theory
Signal Processing
url https://arxiv.org/abs/2410.16140