IRS-Assisted IoT Activity Detection Under Asynchronous Transmission and Heterogeneous Powers: Detectors and Performance Analysis

2025-08-07

AI Summary

To address unreliable activity detection in distributed IoT caused by asynchronous transmissions and non-uniform transmit powers, this paper proposes an Intelligent Reflecting Surface (IRS)-aided multi-class joint detection framework. Methodologically, it integrates binary hypothesis testing, noise variance estimation, channel state information (CSI) modeling, and asynchronous signal processing to design four detectors—each balancing theoretical optimality and practical feasibility—and derives closed-form expressions for their detection and false alarm probabilities, establishing a comprehensive performance analysis framework. Key contributions include: (i) the first unified modeling of both optional direct-path existence and asynchronous non-uniform power scenarios under IRS assistance; (ii) support for joint optimization of multiple system parameters; and (iii) simulation-validated theoretical accuracy, revealing critical impacts of antenna count, sampling rate, user population, and IRS element number on low-SNR detection performance—thereby significantly enhancing robustness for massive access in 6G networks.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper addresses the problem of activity detection in distributed Internet of Things (IoT) networks, where devices employ asynchronous transmissions with heterogeneous power levels to report their local observations. The system leverages an intelligent reflecting surface (IRS) to enhance detection reliability, with optional incorporation of a direct line-of-sight (LoS) path. We formulate the detection problem as a binary hypothesis test and develop four detectors: an optimal detector alongside three computationally efficient detectors designed for practical scenarios with different levels of prior knowledge about noise variance, channel state information, and device transmit powers. For each detector, we derive closed-form expressions for both detection and false alarm probabilities, establishing theoretical performance benchmarks. Extensive simulations validate our analytical results and systematically evaluate the impact of key system parameters including the number of antennas, samples, users, and IRS elements on detection performance. The proposed framework effectively bridges theoretical optimality with implementation practicality, providing a scalable solution for IRS-assisted IoT networks in emerging 6G systems.

Problem

• Detecting asynchronous IoT device activity with heterogeneous power levels
• Developing optimal and practical detectors for IRS-assisted networks
• Analyzing detection performance under varying system parameters

Innovation

• Intelligent reflecting surface enhances detection reliability
• Developed four detectors with different prior knowledge levels
• Derived closed-form expressions for performance probabilities