Tensor-Based Channel Estimation and Reflection Design for RIS-Aided Millimeter-Wave MIMO Communication Systems

TL;DR

This paper introduces a tensor-based channel estimation method (TenRICE) and a reflection design strategy (FroMax) for RIS-aided mmWave MIMO systems, improving estimation accuracy and reducing complexity.

Contribution

It proposes a novel tensor decomposition approach for channel estimation and a non-iterative reflection design method, enhancing efficiency and performance in RIS-assisted mmWave MIMO systems.

Findings

TenRICE approaches the Cramér-Rao lower bound with low training overhead.

FroMax achieves comparable performance to benchmarks with lower complexity.

The methods outperform existing benchmark techniques in simulations.

Abstract

In this work, we consider both channel estimation and reflection design problems in point-to-point reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) MIMO communication systems. First, we show that by exploiting the low-rank nature of mmWave MIMO channels, the received training signals can be written as a low-rank multi-way tensor admitting a canonical polyadic CP decomposition. Utilizing such a structure, a tensor-based RIS channel estimation method (termed TenRICE) is proposed, wherein the tensor factor matrices are estimated using an alternating least squares method. Using TenRICE, the transmitter-to-RIS and the RIS-to-receiver channels are efficiently and separately estimated, up to a trivial scaling factor. After that, we formulate the beamforming and RIS reflection design as a spectral efficiency maximization problem. Due to its non-convexity, we propose a heuristic non-iterative two-step method, where the RIS reflection vector is obtained in a closed form using a Frobenius-norm maximization (FroMax) strategy. Our numerical results show that TenRICE has a superior performance, compared to benchmark methods, approaching the Cram\'er-Rao lower bound with a low training overhead. Moreover, we show that FroMax achieves a comparable performance to benchmark methods with a lower complexity.