Truncated Beam Sweeping for Spatial Covariance Matrix Reconstruction in Hybrid Massive MIMO

TL;DR

This paper introduces a truncated beam sweeping algorithm for efficient spatial covariance matrix reconstruction in hybrid massive MIMO systems, reducing computational complexity while maintaining performance.

Contribution

It proposes a truncated BSA that leverages antenna pattern characteristics to speed up SCM reconstruction in hybrid massive MIMO.

Findings

Truncated BSA reduces sweeping time significantly.

Performance loss is negligible with the proposed method.

Analysis shows the impact of truncation on reconstruction accuracy.

Abstract

Spatial covariance matrix (SCM) is essential in many applications of multi-antenna systems such as massive multiple-input multiple-output (MIMO). For massive MIMO operating at millimeter-wave bands, hybrid analog-digital structure has been adopted to reduce the cost of radio frequency (RF) chains. In this situation, signals received at the antennas are unavailable to the digital receiver, and as a consequence, traditional sample average approach cannot be used for SCM reconstruction in hybrid massive MIMO. To address this issue, beam sweeping algorithm (BSA), which can reconstruct SCM effectively in hybrid massive MIMO, has been proposed in our previous work. In this paper, a truncated BSA is further proposed for SCM reconstruction by taking into account the patterns of antenna elements in the array. Due to the directive antenna pattern, sweeping results corresponding to predetermined direction-of-angles (DOA) far from the normal direction are small and thus can be replaced by predetermined constants. At the cost of negligible performance reduction, SCM can be reconstructed efficiently by sweeping only the predetermined DOAs that are close to the normal direction. In this way, BSA can be conducted much faster than its traditional counterpart. Insightful analysis will be also included to show the impact of truncation on the performance.