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Radio resource management in cellular networks is typically based on devicemeasurements reported to the serving base station. Frequent measuring of signalquality on available frequencies would allow for highly reliable networks andoptimal connection at all times. However, these measurements are associatedwith costs, such as dedicated device time for performing measurements when thedevice will be unavailable for communication. To reduce the costs, we considerpredictions of inter-frequency radio quality measurements that are useful toassess potential inter-frequency handover decisions. In this contribution, wehave considered measurements from a live 3GPP LTE network. We demonstrate thatstraightforward applications of the most commonly used machine learning modelsare unable to provide high accuracy predictions. Instead, we propose a novelapproach with a duo-threshold for high accuracy decision recommendations. Ourapproach leads to class specific prediction accuracies as high as 92% and 95%,still drastically reducing the need for inter-frequency measurements.

Inter-frequency radio signal quality prediction for handover, evaluated in 3GPP LTE