Machine learning based signal strength and uncertainty prediction for MEC mobility management

This paper addressed mobile edge computing (MEC) mobility management using machine learning methods. The mobility decision was based on a reference signal received power (RSRP) value and uncertainty predictor. Neural networks (NNs) were used to establish the predictor, which output the RSRP mean values and standard deviations of different neighbor cells. Closed-form expressions of handover probabilities were derived. With these probabilities, the MEC server was able to cache user services in advance in order to minimize disruptions during handover. Real field data were collected in a typical dense urban area and used to evaluate the performance of the proposed algorithm.