Windowing Optimization for Fingerprint-Spectrum-Based Passive Sensing in Perceptive Mobile Networks

TL;DR

This paper investigates optimal window functions to enhance synchronization accuracy in passive sensing for 6G mobile networks, addressing velocity and range ambiguities caused by asynchronicity.

Contribution

It derives a near-optimal window function for synchronization and evaluates a practical window using MUSIC algorithm, improving passive sensing performance.

Findings

Derived a near-optimal window with theoretical MSE analysis

Proposed a practical window function tested with MUSIC algorithm

Achieved improved synchronization accuracy in passive sensing

Abstract

Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical "window function" by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance.