Recurrent Neural Networks for Handover Management in Next-Generation Self-Organized Networks

TL;DR

This paper introduces a machine learning-based handover management scheme for next-generation self-organized networks, utilizing LSTM models to improve user experience and download efficiency during cell transitions.

Contribution

It proposes novel LSTM-based models for handover decision-making that leverage experience data, outperforming traditional schemes in key performance metrics.

Findings

18% increase in successful downloads

Reduced download time compared to standard schemes

Model generalizes well to different scenarios

Abstract

In this paper, we discuss a handover management scheme for Next Generation Self-Organized Networks. We propose to extract experience from full protocol stack data, to make smart handover decisions in a multi-cell scenario, where users move and are challenged by deep zones of an outage. Traditional handover schemes have the drawback of taking into account only the signal strength from the serving, and the target cell, before the handover. However, we believe that the expected Quality of Experience (QoE) resulting from the decision of target cell to handover to, should be the driving principle of the handover decision. In particular, we propose two models based on multi-layer many-to-one LSTM architecture, and a multi-layer LSTM AutoEncoder (AE) in conjunction with a MultiLayer Perceptron (MLP) neural network. We show that using experience extracted from data, we can improve the number of users finalizing the download by 18%, and we can reduce the time to download, with respect to a standard event-based handover benchmark scheme. Moreover, for the sake of generalization, we test the LSTM Autoencoder in a different scenario, where it maintains its performance improvements with a slight degradation, compared to the original scenario.