1 research outputs found
Deep-Mobility: A Deep Learning Approach for an Efficient and Reliable 5G Handover
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.Comment: This paper was accepted at the 29th ICCCN 202