Review of “Active IoT User Detection in Near-Field with Location Information”

Overall assessment

Technically solid and promising, but requires major revisions—principally adding reproducibility details, statistical reporting, at least one established baseline and runtime/scalability data, and improved clarity—before it is suitable for publication.

Strengths

The paper addresses a timely problem (near-field AUD for grant-free IoT) with a clear motivation, a sound mathematical formulation (Rician near-field model and ADMM-based solver), closed-form ADMM updates, and an informative simulation study showing when location information benefits detection.

Points to improve

The manuscript lacks sufficient experimental reproducibility and statistical rigor (missing simulation details, no error bars, limited baselines and runtime data) and needs clearer presentation of contributions, setup, and results.

Recommendations

• Provide a complete, detailed simulation plan in Methods that specifies user/antenna/pilot counts, SNR ranges, distributions of user locations, how location-estimation errors are generated and parameterized, the exact number of Monte Carlo trials with fixed random seeds, and the software/library and runtime environment used.
• Ensure fair, transparent comparisons by detailing and matching hyperparameters, stopping criteria, initializations, and computational budgets/solver settings across methods; include at least one additional established AUD baseline (e.g., OMP, AMP, or a Bayesian method) or provide a clear justification for its exclusion.
• Report standard evaluation metrics and their uncertainty: present missed-detection and false-alarm rates (or ROC/precision–recall), channel-estimation MSE where applicable, runtime, and include confidence intervals or error bars on all figures; state how curve values were computed (mean over trials) and add a numeric table for key operating points (mean ± SE or 95% CI, sample size).
• Add ablation studies that vary LoS/NLoS strength and location error to identify operating regimes where location information provides benefit and where it does not.
• Strengthen the Introduction by explicitly stating how the research addresses identified gaps—particularly IoT integration, near-field AUD challenges, and limitations of current AUD methods—cite specific prior work that you build on or differ from, and make aims and expected outcomes more specific.

Paper summary

This paper investigates active user detection (AUD) in near-field Internet of Things (IoT) networks by leveraging prior knowledge of users' locations. The authors propose a method where the base station (BS) utilizes location estimates to reconstruct line-of-sight (LoS) channel components, enhancing the AUD process. The method is formulated as a convex optimization problem and solved using the alternating direction method of multipliers (ADMM). Simulation results indicate that the proposed approach significantly outperforms the baseline method that does not utilize location information, particularly under conditions of strong LoS and perfect location estimation.

Main claims

• The proposed method significantly enhances active user detection (AUD) in near-field IoT networks by utilizing users' location information.
• The method achieves higher accuracy in detecting active users compared to the baseline approach that does not use location data.
• The performance of the proposed method remains robust even with imperfect location estimates, provided the estimation error is within acceptable bounds.

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.