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

• URL: http://arxiv.org/abs/2101.06558v2
• Date: Tue, 19 Jan 2021 01:19:11 GMT
• Title: Deep-Mobility: A Deep Learning Approach for an Efficient and Reliable 5G Handover
• Authors: Rahul Arun Paropkari, Anurag Thantharate, Cory Beard
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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