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

2025/08/08 by Amirhossein Taherpour, Somayeh Khani, Taherpour, Amirhossein +5
Computer Science · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #IoT and Edge/Fog Computing #Neural Networks and Applications #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2508.05959

openalex publication_date 2025/08/08 · openalex created_date 2025/10/15 · openalex updated_date 2026/07/28

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.

Citations

• Mixture of Experts-augmented Deep Unfolding for Activity Detection in IRS-aided Systems
• Wireless Channel Measurements and Characterization in Industrial IoT Scenarios
• Multi-Functional RIS-Assisted Semantic Anti-Jamming Communication and Computing in Integrated Aerial-Ground Networks
• Large Array Antenna Spectrum Sensing in Cognitive Radio Networks
• Activity Detection for Grant-Free NOMA in Massive IoT Networks
• A Random Access Protocol for RIS-Aided Wireless Communications
• Active IRS Aided Multiple Access for Energy-Constrained IoT Systems
• A Bayesian Tensor Approach to Enable RIS for 6G Massive Unsourced Random Access
• ML and MAP Device Activity Detections for Grant-Free Massive Access in Multi-Cell Networks
• NOMA for Next-generation Massive IoT: Performance Potential and Technology Directions
• Reconfigurable Intelligent Surface for Massive Connectivity
• Grant-Free Random Access in Machine-Type Communication: Approaches and Challenges
• Grant-free Non-orthogonal Multiple Access for IoT: A Survey
• The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses

Related

• Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation
• Downlink Received Power Performance Analysis of IRS for 6G Networks
• An Overview on IRS-Enabled Sensing and Communications for 6G: Architectures, Fundamental Limits, and Joint Beamforming Designs
• Power Optimization and Deep Learning for Channel Estimation of Active IRS-Aided IoT
• A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication Networks