UE STATISTICS TIME-SERIES (CQI) IN LTE NETWORKS

In our work, we consider such a case, where network access is provided to the end-users via heterogeneous (3GPP and non-3GPP) Distributed Units (DUs), converging to a single Central Unit (CU), and programmable on the fly with external interfaces. We employ Machine Learning (ML) methods in order to forecast the Quality of Service (QoS) that a wireless client will get from the network in the near future based on the Channel State Information (CSI) metric.

Subsequently, we appropriately steer the traffic over the different heterogeneous DUs for ensuring that the network meets the needs of the UEs. We design, develop, deploy and evaluate our method in a real testbed environment, using emulated mobility. Our results show that the overall throughput of each UE can be drastically improved compared to existing allocation mechanisms.

This work is based on the submitted data in this repository, where we collect Channel Quality Indicator (CQI) data from real commercial networks in city Volos, in Greece. Specifically, we create a dataset with CQI data from 74 cars driving through a specific road in the city.