Self-Supervised Transformer Architecture for Change Detection in Radio Access Networks
This work addresses the challenge of monitoring frequent and heterogeneous changes in telecommunications networks for network operators, which is crucial for optimizing performance, especially with the adoption of 5G/6G networks.
The paper tackles the problem of detecting changes in Radio Access Networks (RANs) by proposing a self-supervised learning framework that leverages self-attention and self-distillation, achieving a 4% improvement over state-of-the-art methods on a real-world dataset with about 100,000 time series.
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.