Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications

Abstract

The use of efficient beam tracking mechanisms becomes necessary in highly directional communication of the fifth‐generation systems. In this context, this paper analyzes the tracking problem and proposes a framework that exploits the samples in the user equipment (UE) dataset (historical UE dataset) to efficiently estimate and predict the channel state at the base station. The framework is composed of two steps. First, a supervised learning algorithm, namely, K‐nearest neighbors (K‐NN), is evaluated as a mean of (i) finding the most similar historical samples to some beam measurements reported by the UE and (ii) predicting the corresponding channel state. As a second step, a sampling and reconstruction strategy based on graph signal processing (GSP) called K‐nearest neighbors with reconstruction (K‐NN‐R) is introduced in order to reduce the beam search space during the beam measurement stage, which allows a more efficient usage of the feedback channel. Simulation results illustrate the performance of the proposal in terms of normalized mean square error in comparison with three traditional/baseline prediction techniques. The K‐NN technique provides a better performance than the baseline approaches for any length of the observed temporal window and with full beam sweep. Meanwhile, the K‐NN‐R framework outperforms the baseline approaches with only half of the beam pairs and throughout a significantly low length of observed temporal window.

Machine Learning/AI for IoT, M2M, and Computer Communication

“…In this SI, the first two papers investigate the use of ML in beamforming for directional communication. The first paper, “Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications” by Ortega et al, applies supervised learning to identify historically similar samples and predict channel state. Then K‐nearest neighbor (K‐NN) is used to reduce beam search spaces such that feedback channel usage is improved.…”