A PDD-Inspired Channel Estimation Scheme in NOMA Network

Abstract

In 5G networks, non-orthogonal multiple access (NOMA) provides a number of benefits by providing uneven power distribution to multiple users at once. On the other hand, effective power allocation, successful successive interference cancellation (SIC), and user fairness all depend on precise channel state information (CSI). Because of dynamic channels, imperfect models, and feedback overhead, CSI prediction in NOMA is difficult. Our aim is to propose a CSI prediction technique based on an ML model that accounts for partially decoded data (PDD), a byproduct of the SIC process. Our proposed technique has been shown to be efficient in handover failure (HOF) prediction and reducing pilot overhead, which is particularly important in 5G. We have shown how machine learning (ML) models may be used to forecast CSI in NOMA handover.