An Efficient Beam and Channel Acquisition via Sparsity Map and Joint Angle-Delay Power Profile Estimation for Wideband Massive MIMO Systems

TL;DR

This paper introduces an efficient method for beam and channel acquisition in wideband massive MIMO systems using sparsity maps and joint angle-delay power profile estimation, improving accuracy and reducing training overhead.

Contribution

It proposes a novel sparsity map construction and joint angle-delay power profile estimation method for hybrid beamforming in wideband massive MIMO systems, with two operational modes.

Findings

Achieves near-MMSE channel estimation accuracy.

Reduces training snapshots significantly.

Effective in multi-user interference scenarios.

Abstract

In this paper, an efficient beam and channel acquisition scheme together with joint angle-delay power profile (JADPP) construction are proposed for single-carrier mm-wave wideband sparse massive multiple-input multiple-output (MIMO) channels when hybrid beamforming architecture is utilized. We consider two different modes of operation, namely slow-time beam acquisition and fast-time instantaneous channel estimation, for training stage of time division duplex based systems. In the first mode, where pre-structured hybrid beams are formed to scan intended angular sectors, the joint angle-delay sparsity map together with power intensities of each user channels are obtained by using a novel constant false alarm rate thresholding algorithm inspired from adaptive radar detection theory. The proposed thresholding algorithm employs a spatio-temporal adaptive matched filter type estimator, taking the strong interference due to simultaneously active multipath components of different user channels into account, in order to estimate JADPP of each user. After applying the proposed thresholding algorithm on the estimated power profile, the angle-delay sparsity map of the massive MIMO channel is constructed, based on which the channel covariance matrices (CCMs) are formed with significantly reduced amount of training snapshots. Then, by using the estimated CCMs, the analog beamformer is reconstructed by means of a virtual sectorization while taking the inter-group and inter-symbol interference into account. Finally, for the second mode of operation, two novel reduced-rank instantaneous channel estimators, operating in a proper beamspace formed by the hybrid structure, are proposed. The proposed beam and channel acquisition techniques attain the channel estimation accuracy of minimum mean square error filter with true knowledge of CCMs.