Energy and spectral efficiency optimization using probabilistic based spectrum slicing (PBSS) in different zones of 5G wireless communication network

Spectrum Slicing is arising as an important notion for 5G wireless network as it helps in increasing the data rate, capacity and therefore energy efficiency and spectral efficiency of 5G network. In this paper, traffic modelling is done on the basis of user density and demand. The system model for spectrum slicing is analyzed on the basis of traffic density pattern analysis so that utilization of spectrum are based on probability of active users in different zones i.e. urban, sub-urban and rural area which has the objective of increasing spectral efficiency. Moreover, Hidden Markov Model is used for training and preserving of Base station such that probabilistic spectrum allocation to different user densities can be achieved which aims to use the spectrum efficiently. Novel spectrum slicing technique can contribute a platform for people belonging to Below Poverty Line such that they can make use of spectrum freely. This approach not only reduce the wastage of spectrum but also reduces the interference and hence enhances the spectral efficiency and energy efficiency which optimizes the power so that high QoE and QoS can be achieved.