An Efficient CSI Acquisition Method for Intelligent Reflecting Surface-assisted mmWave Networks

Abstract

Millimeter-wave (mmWave) communication is one of the key enablers of the fifth-generation cellular networks (5G). However, one of the fundamental challenges of mmWave communication is the susceptibility to blockage effects. One way to alleviate this effect is the use of Intelligent Reflecting Surface (IRS). Nevertheless, due to the large number of the reflecting elements on IRS, the channel estimation turns out to be a challenging problem. This paper proposes a low-complexity channel information acquisition method for IRS-assisted mmWave communication system. Our idea consists in exploiting the sparsity of the mmWave channel and the topology of IRS itself. Compared to the state-of-the-art methods, our proposed method requires much less time-frequency resource in the acquisition of channel information. Large performance gains are confirmed in simulation, which prove the effectiveness of our proposed method.

Submitted to ICC2020

References in corpus (3)

• An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
• A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems
• Beyond Massive-MIMO: The Potential of Positioning with Large Intelligent Surfaces

Cited by in corpus (5)

• Channel Estimation for Intelligent Reflecting Surface Assisted MIMO Systems: A Tensor Modeling Approach
• Machine Learning for User Partitioning and Phase Shifters Design in RIS-Aided NOMA Networks
• Millimeter Wave Communications With Reconfigurable Intelligent Surface: Performance Analysis and Optimization
• Joint Power Allocation and User Association Optimization for IRS-Assisted mmWave Systems
• RIS Configuration, Beamformer Design, and Power Control in Single-Cell and Multi-Cell Wireless Networks