  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.
