Tabular and Time-Series Position Encodings in 6G Network Data

Abstract

Sixth-generation (6G) networks collect positioning data that must be transformed into a suitable representation before machine-learning models can use it effectively. The choice of this encoding is rarely treated as an experimental variable, yet it strongly shapes what information reaches the downstream model. This paper evaluates how tabular and time-series encoding techniques affect beam prediction performance and feature representation quality in nine scenarios from the DeepSense 6G dataset. Beam-prediction performance is measured using two downstream classifiers in a fixed multi-seed evaluation pipeline, while representation quality is assessed through invariance under positional noise. Encodings that represent the user equipment relative to the base station and include temporal context achieve the best performance. However, the representation analysis reveals that these geometry-aware encodings are less stable under positional noise. The findings suggest that, when position estimates are accurate, position and trajectory data should be encoded using base-station-relative distance, bearing and recent geometric change, whereas noisier settings may require additional preprocessing to preserve robustness.