Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach

2022/03/30 by Zheng, Xi, Wang, Peilan, Fang, Jun +1 · 2 citations
#FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2203.16164

Abstract

We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our proposed method is as low as O(U²), where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method.

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• Near-Field Sparse Bayesian Channel Estimation and Tracking for XL-IRS-Aided Wideband mmWave Systems
• RIS- and Multi-Snapshot-Enabled SISO 3D Position and Velocity Estimation With Single Base Station

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