Joint Estimation of Multipath Angles and Delays for Millimeter-Wave Cylindrical Arrays with Hybrid Front-ends

TL;DR

This paper introduces a novel joint delay and angle estimation method for wideband mmWave cylindrical arrays with hybrid front-ends, effectively addressing beam squint and RF chain limitations.

Contribution

It proposes a new hybrid beamformer design and a subspace-based algorithm that accurately estimates multipath parameters with reduced complexity and fewer RF chains.

Findings

Approaches the Cramér-Rao lower bound in estimation accuracy.

Reduces computational complexity compared to existing methods.

Effectively suppresses beam squint in wideband mmWave systems.

Abstract

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This paper presents a novel joint delay and angle estimation approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cram\'{e}r-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.