Initiating Automated Handoff in Wireless Networks Based on Neural Network Enabled CSI Estimation

Machine Learning and Deep Learning algorithms are being extensively used for optimizing software defined networks. With increasing number of users and multimedia applications, bandwidth efficiency in cellular networks has become a critical aspect for system design. Bandwidth is a vital resource shared by wireless networks. Hence its in critical to enhance bandwidth efficiency. Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple access (NOMA) have been the leading contenders for modern wireless networks. NOMA is a technique in which multiple users data is separated in the power domain. In the proposed approach , a machine learning based handover mechanism between OFDM and NOMA has been proposed based on channel conditions. The condition for switching or handover has been chosen as the BER of the system. A comparative analysis with existing work indicates that the proposed scheme outperforms the existing techniques in terms of SNR requirement thereby making the system more practically useful for fading channel conditions.