Learning Low-Dimensional Representation for O-RAN Testing via Transformer-ESN

TL;DR

This paper presents a two-stage framework using Transformer-ESN to learn low-dimensional, temporally-aware representations of O-RAN KPIs, improving testing efficiency and accuracy especially with limited data.

Contribution

It introduces a novel Transformer-ESN model with an information-theoretic training approach for effective low-dimensional KPI representation in O-RAN testing.

Findings

Up to 41.9% MSE reduction in RSRQ prediction with limited samples.

Significant improvements in spectral efficiency prediction accuracy.

Framework reduces testing complexity for O-RAN systems.

Abstract

Open Radio Access Network (O-RAN) architectures enhance flexibility for 6G and NextG networks. However, it also brings significant challenges in O-RAN testing with evaluating abundant, high-dimensional key performance indicators (KPIs). In this paper, we introduce a novel two-stage framework to learn temporally-aware low-dimensional representations of O-RAN testing KPIs. To be specific, stage one employs an information-theoretic H-score to train a hybrid self-attentive transformer and echo state network (ESN) reservoir, called Transformer-ESN, capturing temporal dynamics and producing task-aligned $8$-dimensional embeddings. Stage two evaluates these embeddings by training a lightweight multilayer perceptron (MLP) predictor exclusively on them for key target KPIs such as reference signal received quality (RSRQ) and spectral efficiency. Using real-world O-RAN testbed data (video streaming with interference), our approach demonstrates a significant advantage specifically when training samples are very limited. In this scenario, the low-dimensional representations learned from the Transformer-ESN yield mean square error (MSE) reductions of up to 41.9\% for RSRQ and 29.9\% for spectral efficiency compared to predictions from the original high-dimensional data. The framework exhibits high efficiency for O-RAN testing, significantly reducing testing complexities for O-RAN systems.