Tracking mm-Wave Channel Dynamics: Fast Beam Training Strategies under Mobility

TL;DR

This paper introduces innovative beam training and tracking strategies for mm-wave systems that maintain high data rates under mobility by utilizing hybrid transceivers and data-driven updates, significantly outperforming existing methods.

Contribution

It presents a novel probabilistic optimization framework and a data packet-based beam tracking algorithm that reduces complexity and improves performance in mobile mm-wave communications.

Findings

Achieves only 10% rate loss compared to the optimal.

Provides 40% to 150% higher data rates than current methods.

Requires lower hardware complexity for implementation.

Abstract

In order to cope with the severe path loss, millimeter-wave (mm-wave) systems exploit highly directional communication. As a consequence, even a slight beam misalignment between two communicating devices (for example, due to mobility) can generate a significant signal drop. This leads to frequent invocations of time-consuming mechanisms for beam re-alignment, which deteriorate system performance. In this paper, we propose smart beam training and tracking strategies for fast mm-wave link establishment and maintenance under node mobility. We leverage the ability of hybrid analog-digital transceivers to collect channel information from multiple spatial directions simultaneously and formulate a probabilistic optimization problem to model the temporal evolution of the mm-wave channel under mobility. In addition, we present for the first time a beam tracking algorithm that extracts information needed to update the steering directions directly from data packets, without the need for spatial scanning during the ongoing data transmission. Simulation results, obtained by a custom simulator based on ray tracing, demonstrate the ability of our beam training/tracking strategies to keep the communication rate only 10% below the optimal bound. Compared to the state of the art, our approach provides a 40% to 150% rate increase, yet requires lower complexity hardware.