Fast Channel Estimation in the Transformed Spatial Domain for Analog Millimeter Wave Systems

TL;DR

This paper introduces a novel transformed spatial domain approach for fast and robust channel estimation in millimeter-wave systems, reducing complexity and improving accuracy over traditional methods.

Contribution

The paper proposes the TSDCE algorithm that moves channel estimation into the transformed spatial domain, enhancing robustness and reducing computational complexity.

Findings

TSDCE achieves lower mean square error in channel estimation.

The method demonstrates robustness to additive white Gaussian noise.

It significantly reduces computational complexity compared to benchmarks.

Abstract

Fast channel estimation in millimeter-wave (mmWave) systems is a fundamental enabler of high-gain beamforming, which boosts coverage and capacity. The channel estimation stage typically involves an initial beam training process where a subset of the possible beam directions at the transmitter and receiver is scanned along a predefined codebook. Unfortunately, the high number of transmit and receive antennas deployed in mmWave systems increase the complexity of the beam selection and channel estimation tasks. In this work, we tackle the channel estimation problem in analog systems from a different perspective than used by previous works. In particular, we propose to move the channel estimation problem from the angular domain into the transformed spatial domain, in which estimating the angles of arrivals and departures corresponds to estimating the angular frequencies of paths constituting the mmWave channel. The proposed approach, referred to as transformed spatial domain channel estimation (TSDCE) algorithm, exhibits robustness to additive white Gaussian noise by combining low-rank approximations and sample autocorrelation functions for each path in the transformed spatial domain. Numerical results evaluate the mean square error of the channel estimation and the direction of arrival estimation capability. TSDCE significantly reduces the first, while exhibiting a remarkably low computational complexity compared with well-known benchmarking schemes.