Multilinear SVD for Millimeter Wave Channel Parameter Estimation

TL;DR

This paper presents a multilinear SVD-based method for efficient millimeter wave channel parameter estimation in 5G systems, achieving near-optimal accuracy with reduced computational complexity.

Contribution

It introduces a tensor-based MSVD approach for channel estimation in hybrid beamforming MIMO systems, addressing high-dimensional measurement challenges.

Findings

Method closely matches the Cramer-Rao bound

Provides computational efficiency over existing methods

Analyzes effects of waveform and estimation limitations

Abstract

Fifth generation (5G) cellular standards are set to utilize millimeter wave (mmWave) frequencies, which enable data speeds greater than 10 Gbps and sub-centimeter localization accuracy. These capabilities rely on accurate estimates of the channel parameters, which we define as the angle of arrival, angle of departure, and path distance for each path between the transmitter and receiver. Estimating the channel parameters in a computationally efficient manner poses a challenge because it requires estimation of parameters from a high-dimensional measurement -- particularly for multi-carrier systems since each subcarrier must be estimated separately. Additionally, channel parameter estimation must be able to handle hybrid beamforming, which uses a combination of digital and analog beamforming to reduce the number of required analog to digital converters. This paper introduces a channel parameter estimation technique based on the multilinear singular value decomposition (MSVD), a tensor analog of the singular value decomposition, for massive multiple input multiple output (MIMO) multi-carrier systems with hybrid beamforming. The MSVD tensor estimation approach provides a computationally efficient method and is shown to closely match the Cramer-Rao bound (CRB) of parameter estimates through simulations. Limitations of channel parameter estimation and communication waveform effects are also studied.