Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

TL;DR

This paper proposes a novel two-stage channel estimation framework for large intelligent metasurface-assisted massive MIMO systems, addressing the challenge of passive reflection and limited processing capability of the metasurface.

Contribution

It introduces a general framework and a two-stage algorithm combining sparse matrix factorization and matrix completion for accurate channel estimation.

Findings

Achieves accurate channel estimation in LIM-assisted massive MIMO.

Demonstrates effectiveness through simulation results.

Addresses passive reflection limitations of metasurfaces.

Abstract

In this letter, we consider the problem of channel estimation for large intelligent metasurface (LIM) assisted massive multiple-input multiple-output (MIMO) systems. The main challenge of this problem is that the LIM integrated with a large number of low-cost metamaterial antennas can only passively reflect the incident signal by a certain phase shift, and does not have any signal processing capability. To deal with this, we introduce a general framework for the estimation of the transmitter-LIM and LIM-receiver cascaded channel, and propose a two-stage algorithm that includes a sparse matrix factorization stage and a matrix completion stage. Simulation results illustrate that the proposed method can achieve accurate channel estimation for LIM-assisted massive MIMO systems.