Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

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 signals by certain phase shifts, 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.

I. Introduction

Massive multiple-input multiple-output (MIMO), as a promising technology for future wireless systems, has attracted growing research interest in both academia and industry over recent years. Although massive MIMO exhibits huge potentials to support a significantly large amount of mobile data traffic and wireless connections, implementing this system with large-scale antenna arrays in practice remains very challenging due to high hardware cost and increased power consumption. To achieve green and sustainable wireless networks, researchers have started looking into energy efficient techniques to improve the system performance, ranging from the utilization of energy efficient hardware components to the design of green resource allocation and transceiver signal processing algorithms.