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

TL;DR

This paper introduces a low-rank tensor decomposition method for efficient downlink channel estimation in IRS-assisted mmWave OFDM systems, leveraging channel sparsity to reduce training overhead.

Contribution

It proposes a structured CPD-based approach that exploits channel sparsity, significantly lowering training overhead in IRS-assisted mmWave OFDM systems.

Findings

Training overhead as low as O(U^2) with channel sparsity U.

Effective channel parameter estimation demonstrated through simulations.

Method leverages tensor decomposition to exploit channel structure.

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(U^2), where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method.