Novel Statistical Modelling and Optimization Techniques of Fading Channel Coefficients for 5G Network Performance

Abstract The channel estimation process (CE) is a pivotal element in wireless communication employing orthogonal frequency division multiplexing (OFDM) modulation. The derived coefficient can represent the channel characterization. A newly proposed model derived for fifth-generation (5G) network and future communication systems two encounter the fading effects of the channel. The proposed model improves the channel estimation accuracy by incorporating precise measurements of channel coefficients, enhancing the overall performance. The model uses least square (LS) and minimum mean squared error (MMSE) optimization algorithms for the channel coefficient extraction process. The effectiveness of the proposed model has been experimentally confirmed using different fading channel scenarios in the literature. The mean square error (MSE) and bit error rate (BER) test criteria are used for performance measures. The test was generalized using a 64, 256 and 1024 quadrature amplitude modulation (QAM) scheme, demonstrating the model’s applicability to various communication scenarios. The proposed model was evaluated alongside several state-of-the-art statistical channel estimation methods to demonstrate its performance across various tests. When compared to pilot-based estimation, the model consistently exhibits superior accuracy and adaptability, ensuring dependable performance in a wide range of communication scenarios. Additionally, the proposed model is better suited to handle the complexity of non-line-of-sight (NLOS) and multipath environments, offering a more nuanced approach compared to pilot signals, which often rely on idealized assumptions of channel linearity.