Proactive Mobility Management of UEs using Sequence-to-Sequence Modeling

TL;DR

This paper introduces an AI-based sequence-to-sequence model for proactive mobility management in 5G networks, predicting handover cells and dwell-times with high accuracy using UE trajectory data.

Contribution

It presents a novel application of sequence-to-sequence modeling for predicting mobility parameters in dense 5G networks, improving handover accuracy and dwell-time estimation.

Findings

Over 90% accuracy in handover cell prediction

Low mean absolute error in dwell-time estimation

Effective for dense network deployments

Abstract

Beyond 5G networks will operate at high frequencies with wide bandwidths. This brings both opportunities and challenges. Opportunities include high throughput connectivity with low latency. However, one of the main challenges in these networks is due to the high path loss at operating frequencies, which requires network to be deployed densely to provide coverage. Since these cells have small inter-site-distance (ISD), the dwell-time of the UEs in these cells are small, thus supporting mobility in these types of dense networks is a challenge and require frequent beam or cell reassignments. A pro-active mobility management scheme which exploits the trajectory can provide better prediction of cells and beams as UEs move in the coverage area. We propose an AI based method using sequence-to-sequence modeling for the estimation of handover cells/beams along with dwell-time using the trajectory information of the UE. Results indicate that for a dense deployment an accuracy of more than 90 percent can be achieved for handover cell estimation with very low mean absolute error (MAE) for dwell-time.