Beam Index Map Prediction in Unseen Environments from Geospatial Data

TL;DR

This paper introduces a CNN-based method that uses geospatial data to predict beam distributions in 5G networks, enabling efficient beam training in new environments without prior site-specific training.

Contribution

The proposed approach is the first to leverage geospatial data for generalizing beam prediction in unseen environments without specialized sensors.

Findings

Reduces candidate beams significantly

Improves beam training efficiency

Generalizes to new environments without site-specific training

Abstract

In 5G, beam training consists of the efficient association of users to beams for a given beamforming codebook used at the base station and the given propagation environment in the cell. We propose a convolutional neural network approach that leverages the position of the base station and geospatial data to predict beam distributions for all user locations simultaneously. Our method generalizes to unseen environments without site-specific training or specialized sensors. The results show that it significantly reduces the number of candidate beams considered, thereby improving the efficiency of beam training.