Multistatic Sensing of Passive Targets Using 6G Cellular Infrastructure

TL;DR

This paper introduces CsiSenseNet, an AI-based method for passive target detection and localization using 6G cellular infrastructure, achieving high accuracy and sub-meter precision indoors without target cooperation.

Contribution

It presents a novel AI architecture for passive sensing with 6G systems, enabling target detection and positioning without target assistance.

Findings

High accuracy detection of human-sized targets

Sub-meter positioning errors achieved indoors

Effective use of wideband 6G mmWave channels

Abstract

Sensing using cellular infrastructure may be one of the defining feature of sixth generation (6G) wireless systems. Wideband 6G communication channels operating at higher frequency bands (upper mmWave bands) are better modeled using clustered geometric channel models. In this paper, we propose methods for detection of passive targets and estimating their position using communication deployment without any assistance from the target. A novel AI architecture called CsiSenseNet is developed for this purpose. We analyze the resolution, coverage and position uncertainty for practical indoor deployments. Using the proposed method, we show that human sized target can be sensed with high accuracy and sub-meter positioning errors in a practical indoor deployment scenario.