Iterative Eigenvalue Decomposition and Multipath-Grouping Tx/Rx Joint Beamforming for Millimeter-Wave Communication

TL;DR

This paper proposes novel beamforming schemes for millimeter-wave communications that leverage iterative eigenvalue decomposition and multipath grouping to enhance array and diversity gains, reducing overhead and complexity.

Contribution

It introduces a sub-optimal beamforming method using iterative eigenvalue decomposition with a practical training approach, and a multipath grouping scheme to improve performance and reliability.

Findings

Iterative EVD scheme achieves performance comparable to state-of-the-art with less overhead.

MPG scheme outperforms existing methods with similar complexity.

Proposed methods enhance system reliability in fast fading scenarios.

Abstract

We investigate Tx/Rx joint beamforming in millimeter-wave communications (MMWC). As the multipath components (MPCs) have different steering angles and independent fadings, beamforming aims at achieving array gain as well as diversity gain in this scenario. A sub-optimal beamforming scheme is proposed to find the antenna weight vectors (AWVs) at Tx/Rx via iterative eigenvalue decomposition (EVD), provided that full channel state information (CSI) is available at both the transmitter and receiver. To make this scheme practically feasible in MMWC, a corresponding training approach is suggested to avoid the channel estimation and iterative EVD computation. As in fast fading scenario the training approach may be time-consuming due to frequent training, another beamforming scheme, which exploits the quasi-static steering angles in MMWC, is proposed to reduce the overhead and increase the system reliability by multipath grouping (MPG). The scheme first groups the MPCs and then concurrently beamforms towards multiple steering angles of the grouped MPCs, so that both array gain and diversity gain are achieved. Performance comparisons show that, compared with the corresponding state-of-the-art schemes, the iterative EVD scheme with the training approach achieves the same performance with a reduced overhead and complexity, while the MPG scheme achieves better performance with an approximately equivalent complexity.