A Deep-SIC Channel Estimator Scheme in NOMA Network

TL;DR

This paper introduces Deep-SIC, a Transformer-based channel prediction model utilizing SIC data to improve handover decisions in NOMA networks, significantly reducing failure rates and enhancing stability in high-mobility scenarios.

Contribution

The paper presents a novel Deep-SIC model that leverages SIC data for fast, stable, and accurate channel prediction, outperforming existing algorithms in speed and reliability.

Findings

Reduces handover failure rate by up to 40%

Learns 68% faster than state-of-the-art algorithms

Achieves 20% lower NRMSE in low-SNR conditions

Abstract

In 5G and next-generation mobile ad-hoc networks, reliable handover is a key requirement, which guarantees continuity in connectivity, especially for mobile users and in high-density scenarios. However, conventional handover triggers based on instantaneous channel measurements are prone to failures and the ping-pong effect due to outdated or inaccurate channel state information. To address this, we introduce Deep-SIC, a knowledge-based channel prediction model that employs a Transformer-based approach to predict channel quality and optimise handover decisions. Deep-SIC is a unique model that utilises Partially Decoded Data (PDD), a byproduct of successive interference cancellation (SIC) in NOMA, as a feedback signal to improve its predictions continually. This special purpose enables learners to learn quickly and stabilise their learning. Our model learns 68\% faster than existing state-of-the-art algorithms, such as Graph-NOMA, while offering verifiable guarantees of stability and resilience to user mobility (Theorem~2). When simulated at the system level, it can be shown that our strategy can substantially enhance network performance: the handover failure rate can be reduced by up to 40\%, and the ping-pong effect can be mitigated, especially at vehicular speeds (e.g., 60 km/h). Moreover, Deep-SIC has a 20\% smaller normalised root mean square error (NRMSE) in low-SNR situations than state-of-the-art algorithms with linear computational complexity, $O(K)$. This work has introduced a new paradigm for robust and predictive mobility management in dynamic wireless networks.