Robust Channel Estimation in NOMA Systems Using Weinbull M- Transform for Adaptive Filtering

This research paper explores the challenges of adaptive filtering methods used for channel estimation techniques in NOMA (Non-Orthogonal Multiple Access) systems within high-noise and dynamic environments. Conventional methods are often limited by high Bit Error Rate (BER), energy inefficiency, and reduced sum capacity, which can hinder their performance in modern, high-density applications like 5G communication. To address these limitations, the authors introduce the robust Weibull M-transform as an advanced solution for estimating the channel parameters in NOMA system. By incorporating exponential decay and adaptive scaling, this approach minimizes the impact of large scale errors, enhances stability, reduces BER rate, improves energy efficiency, and maximizes sum capacity-making it a strong candidate for use in demanding, contemporary communication networks where traditional methods are less effective.