Temporal Self-organizing Maps for Prediction of Feature Evolution

The future wireless network deployments in 5G and beyond are dense, operating at multiple frequency bands, and support higher capacity by opportunistically selecting among multiple frequency bands. This results in frequent measurement across different frequency bands, increased battery draining of user equipment (UE), excessive traffic in the control plane and higher latency. In this study, we propose spatio-temporal self-organizing maps for predicting the time evolution of multiple downlink and uplink features by computing the Markov order of the multi-variate time series. We develop an algorithm to estimate the Markov order and use it in conjunction with spatio-temporal self-organizing maps to predict signal dynamics. The proposed algorithm is validated against the publicly available data-sets and Ericsson's 5G test-bed data-sets. The proposed algorithm is able to predict signals up to 13 and 28 seconds into the future for fast and slow-moving UEs.