Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Active IoT User Detection in Near-Field with Location Information

Gabriel Martins de Jesus, Richard Demo Souza, Onel Luis Alcaraz López

TL;DR

This paper considers a scenario where users are distributed in a semi-circular area within the Rayleigh distance of a multi-antenna base station (BS), and proposes the BS to use location estimates of the users to reconstruct their line-of-sight (LoS) channel components, hence assisting the AUD process.

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.

Active IoT User Detection in Near-Field with Location Information

TL;DR

This paper considers a scenario where users are distributed in a semi-circular area within the Rayleigh distance of a multi-antenna base station (BS), and proposes the BS to use location estimates of the users to reconstruct their line-of-sight (LoS) channel components, hence assisting the AUD process.

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.
Paper Structure (18 sections, 36 equations, 7 figures, 2 tables, 1 algorithm)

This paper contains 18 sections, 36 equations, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. System Model
  4. Channel model
  5. Signal model
  6. AUD with and without Location Information
  7. Problem formulation
  8. Solution via ADMM
  9. Case without location information
  10. Computational complexity and implementation
  11. Results
  12. Impact of the
  13. Multipath
  14. Imperfect location estimate
  15. Varying pilot length
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Illustration of the system model, where users are indicated by the filled circles, with black filling indicating the $K$ active users.
  • Figure 2: Performance comparison between the baseline and the approach with location information as a function of the SNR.
  • Figure 3: Performance comparison between the baseline and the approaches with perfect and imperfect location information for the case with SNR = $0$ dB as a function of the fading parameter $\mu$.
  • Figure 4: Performance comparison between the baseline and the approaches with perfect and imperfect location information for the case with SNR = $0$ dB as a function of the position error variance.
  • Figure 5: Performance comparison between the baseline and the approaches with perfect and imperfect location information for the case with SNR = $0$ dB as a function of the pilot length.
  • ...and 2 more figures