Channel Parameter Estimation for Millimeter-Wave Cellular Systems with Hybrid Beamforming

TL;DR

This paper introduces a two-stage channel parameter estimation algorithm for millimeter-wave 5G systems using hybrid beamforming, combining coarse DFT-based estimation with refined SAGE optimization, improving accuracy especially at low SNR.

Contribution

A novel two-stage estimation method that enhances channel parameter accuracy in millimeter-wave systems using hybrid beamforming, outperforming existing methods.

Findings

Improved estimation accuracy over ABP method.

Effective in low SNR conditions.

CRLB derived for performance benchmarking.

Abstract

To achieve high data rates defined in 5G, the use of millimeter-waves and massive-MIMO are indispensable. To benefit from these technologies, an accurate estimation of the channel parameters is crucial. We propose a novel two-stage algorithm for channel parameters estimation. In the first stage, coarse estimation is accomplished by applying parameter estimation via interpolation based on DFT grid (PREIDG) with a fixed look-up table (LUT), while the second stage refines the estimates by means of the space-alternating generalized expectation maximization (SAGE) algorithm. The two-stage algorithm uses discrete Fourier transform beamforming vectors which are efficiently implemented by a Butler matrix in the analog domain. We found that this methodology improves the estimates compared to the auxiliary beam pair (ABP) method. The two-stage algorithm shows efficient performance in the low signal to noise ratio regime for the channel parameters i.e. angles of departure, complex path gains and delays of the multipaths. Finally, we derived the Cram\'er-Rao lower bound (CRLB) to assess the performance of our two-stage estimation algorithm.