A PDD-Inspired Channel Estimation Scheme in NOMA Network

TL;DR

This paper introduces a machine learning-based channel estimation scheme for NOMA networks that leverages partially decoded data to improve CSI prediction, reducing overhead and enhancing handover reliability in 5G.

Contribution

It proposes a novel CSI prediction method using PDD-inspired ML models, addressing challenges of dynamic channels and feedback overhead in NOMA.

Findings

Effective in handover failure prediction

Reduces pilot overhead in 5G NOMA

Improves CSI accuracy with PDD-based ML model

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.