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5G cellular networks are being deployed all over the world and thisarchitecture supports ultra-dense network (UDN) deployment. Small cells have avery important role in providing 5G connectivity to the end users. Exponentialincreases in devices, data and network demands make it mandatory for theservice providers to manage handovers better, to cater to the services that auser desire. In contrast to any traditional handover improvement scheme, wedevelop a 'Deep-Mobility' model by implementing a deep learning neural network(DLNN) to manage network mobility, utilizing in-network deep learning andprediction. We use network key performance indicators (KPIs) to train our modelto analyze network traffic and handover requirements. In this method, RF signalconditions are continuously observed and tracked using deep learning neuralnetworks such as the Recurrent neural network (RNN) or Long Short-Term Memorynetwork (LSTM) and system level inputs are also considered in conjunction, totake a collective decision for a handover. We can study multiple parameters andinteractions between system events along with the user mobility, which wouldthen trigger a handoff in any given scenario. Here, we show the fundamentalmodeling approach and demonstrate usefulness of our model while investigatingimpacts and sensitivities of certain KPIs from the user equipment (UE) andnetwork side.

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