Ray-of-Arrival Passing for Indirect Beam Training in Cooperative Millimeter Wave MIMO Networks

TL;DR

This paper introduces RAPID, a cooperative beam training framework for mmWave MIMO networks that uses Ray-based geometry to improve channel estimation and link quality, especially in challenging conditions.

Contribution

The paper presents a novel Ray-of-Arrival Passing for Indirect (RAPID) framework that transforms beamforming into a Euclidean Ray-based domain for cooperative channel estimation.

Findings

RAPID improves average channel estimation accuracy.

RAPID significantly increases the rate of poor quality links.

RAPID enhances the probability of multiple link options at a user.

Abstract

This paper is concerned with the channel estimation problem in multi-cell Millimeter (mmWave) wireless systems. We develop a novel Ray-of-Arrival Passing for Indirect (RAPID) Beam Training framework, in which a network consisting of multiple BS are able to work cooperatively to estimate jointly the UE channels. To achieve this aim, we consider the spatial geometry of the mmWave environment and transform conventional angular domain beamforming concepts into the more euclidean, Ray-based domain. Leveraging this model, we then consider the conditional probabilities that pilot signals are received in each direction, given that the deployment of each BS is known to the network. Simulation results show that RAPID is able to improve the average estimation of the network and significantly increase the rate of poorer quality links. Furthermore, we also show that, when a coverage rate threshold is considered, RAPID is able to improve greatly the probability that multiple link options will be available to a user at any given time.