Active IoT User Detection in Near-Field with Location Information

TL;DR

This paper introduces a location-aware method for active user detection in near-field IoT networks, leveraging prior location knowledge to improve detection accuracy, especially under strong line-of-sight conditions.

Contribution

The paper proposes a convex optimization approach using location estimates to enhance active user detection in near-field IoT, outperforming baseline methods especially with perfect location data.

Findings

Significant performance improvement under perfect location estimation and strong LoS.

Robustness of the method persists with imperfect location estimates within certain bounds.

Higher computational complexity compared to location-agnostic baseline.

Abstract

In this paper, we address active users detection (AUD) in near-field Internet of Things (IoT) networks by exploring prior knowledge of users' locations. We consider a scenario where users are distributed in a semi-circular area within the Rayleigh distance of a multi-antenna base station (BS). We propose the BS to use location estimates of the users to reconstruct their line-of-sight (LoS) channel components, hence assisting the AUD process. For this, the BS combines these reconstructed channels with users' pilot sequences, enhancing the correlation between received signals and active users. We formulate the location-aided AUD as a convex optimization problem, solved via the alternating direction method of multipliers (ADMM). {Our proposal has a higher computational complexity compared to the baseline ADMM approach where location information is not used. Moreover, the proposal requires location information of users, which can be readily informed if users are static, or inferred via established localization algorithms if they are mobile.} Simulation results compare our proposal against the baseline across varying systems parameters, such as number of users, pilot length and LoS component strength. We demonstrate that under perfect location estimation and strong LoS, our proposed method significantly outperforms the baseline. Furthermore, robustness analysis shows that performance gains persist under imperfect location estimation, provided the estimation error remains within bounds determined by the system parameters.