Automatic discovery of sub-optimal radio performance in LTE RAN networks

As telecommunication networks have become large complex systems, it is recognized today that there is an urgent need for providing comprehensive views about how the network operates, instead of fragmented reports about the different elements in the network. We propose a system that holistically analyzes the network along carefully balanced dimensions, both by using historic data and by using data across multiple LTE RAN networks. Our system learns baseline behaviours and provides insights to the operators alerting them about deviations from this learnt behaviour. The system can also help operators by discovering network configurations that lead to good radio performance, which can be used to bootstrap the network configuration process. All the learnt behaviour is made available globally through a global sharing service. We validated our approach using real 4G RAN network data, and show that we discover behaviours in the network which would not be obvious to the operator otherwise.