A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)

摘要

One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper reports a number of significant contributions. First, a novel analytical model is proposed for holistic handover (HO) cost evaluation, that integrates signaling overhead, latency, call dropping, and radio resource wastage. The developed mathematical model is applicable to several cellular architectures, but the focus here is on the Control/Data Separation Architecture (CDSA). Second, data-driven HO prediction is proposed and evaluated as part of the holistic cost, for the first time, through novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Finally, simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be effectively utilized, based on HO management requirements. Both analytical and machine learning models are evaluated with a benchmark, real-world dataset measuring human behaviors and interactions. Numerical and comparative simulation results demonstrate the potential of our proposed deep learning-driven HO management framework, as a future benchmark for the mobile networking and machine learning communities. (C) 2019 The Authors. Published by Elsevier B.V.