Intelligent Reflecting Surface for Massive Device Connectivity: Joint Activity Detection and Channel Estimation

Abstract

Intelligent Reflecting Surface (IRS) has been a promising solution to enhance wireless networks both spectral-efficiently and energy-efficiently. This paper considers an IRS-assisted the Internet of Things network for massive connectivity. We aim to solve the IRS-related activity detection and channel estimation problem which has not been studied before. In this paper, we formulate the IRS-related activity detection and channel estimation problem as sparse matrix factorization, matrix completion and Multiple Measurement Vector problem and, we propose a three-stage framework based on the approximate message passing. Simulation results verify the effectiveness of the proposed algorithm.

References in corpus (4)

• Sparse Activity Detection for Massive Connectivity
• Sparse Signal Processing Concepts for Efficient 5G System Design
• Belief propagation for joint sparse recovery
• Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

Cited by in corpus (2)

• Outage Minimization for Intelligent Reflecting Surface Aided MISO Communication Systems via Stochastic Beamforming
• Stochastic Beamforming for Reconfigurable Intelligent Surface Aided Over-the-Air Computation