Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks

TL;DR

This paper introduces a self-supervised transformer-based framework for detecting changes in Radio Access Networks using performance measurement data, outperforming existing methods and offering scalability and generalizability.

Contribution

It presents a novel self-supervised learning approach utilizing self-attention and self-distillation for change detection in RANs, reducing reliance on expert knowledge.

Findings

Outperforms state-of-the-art by 4% on real-world data

Scalable and generalizable approach

Provides detailed insights into network mode changes

Abstract

Radio Access Networks (RANs) for telecommunications represent large agglomerations of interconnected hardware consisting of hundreds of thousands of transmitting devices (cells). Such networks undergo frequent and often heterogeneous changes caused by network operators, who are seeking to tune their system parameters for optimal performance. The effects of such changes are challenging to predict and will become even more so with the adoption of 5G/6G networks. Therefore, RAN monitoring is vital for network operators. We propose a self-supervised learning framework that leverages self-attention and self-distillation for this task. It works by detecting changes in Performance Measurement data, a collection of time-varying metrics which reflect a set of diverse measurements of the network performance at the cell level. Experimental results show that our approach outperforms the state of the art by 4% on a real-world based dataset consisting of about hundred thousands timeseries. It also has the merits of being scalable and generalizable. This allows it to provide deep insight into the specifics of mode of operation changes while relying minimally on expert knowledge.