Geometric Machine Learning for Channel Covariance Estimation in Vehicular Networks

Abstract

Learning the covariance matrices of spatially-correlated wireless channels, in millimeter-wave (mmWave) vehicular communication, can be utilized in designing environmen-taware beamforming codebooks. Such channel covariance matrices can be represented on non-Euclidean Riemannian manifolds, thanks to their symmetric positive definite (SPD) characteristics. Consequently in this paper, we propose a Riemannian-Geometric machine learning (G-ML) approach for estimating the channel covariance matrices based on unsupervised K-Means model. The proposed K-means algorithm utilizes Log-Euclidean metric (LEM) as the distance measure among channel covariance matrices over the Riemannian manifolds. We show that our proposed K-Means G-ML model can achieve up to 80% less error compared to Euclidean-based K-Means algorithm, which applies clustering on the channel vectors themselves.

6 pages, 8 figures, IEEE conference

References in corpus (5)

• DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications
• Geomstats: A Python Package for Riemannian Geometry in Machine Learning
• Fast and Robust Shortest Paths on Manifolds Learned from Data
• Metric Learning on Manifolds
• Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO