Deep-Mobility: A Deep Learning Approach for an Efficient and Reliable 5G Handover

TL;DR

This paper introduces Deep-Mobility, a deep learning-based model that improves 5G handover management by analyzing network KPIs and RF signal conditions to enable more reliable and efficient handovers in ultra-dense networks.

Contribution

It presents a novel deep learning approach using RNNs and LSTMs for real-time handover decision-making in 5G networks, integrating multiple system parameters.

Findings

Deep-Mobility effectively predicts handover requirements.

The model enhances handover reliability and efficiency.

Sensitivity analysis shows key KPIs impact performance.

Abstract

5G cellular networks are being deployed all over the world and this architecture supports ultra-dense network (UDN) deployment. Small cells have a very important role in providing 5G connectivity to the end users. Exponential increases in devices, data and network demands make it mandatory for the service providers to manage handovers better, to cater to the services that a user desire. In contrast to any traditional handover improvement scheme, we develop a 'Deep-Mobility' model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. In this method, RF signal conditions are continuously observed and tracked using deep learning neural networks such as the Recurrent neural network (RNN) or Long Short-Term Memory network (LSTM) and system level inputs are also considered in conjunction, to take a collective decision for a handover. We can study multiple parameters and interactions between system events along with the user mobility, which would then trigger a handoff in any given scenario. Here, we show the fundamental modeling approach and demonstrate usefulness of our model while investigating impacts and sensitivities of certain KPIs from the user equipment (UE) and network side.