Enhancing Channel Estimation Accuracy in Noisy Environments Using Genetic Algorithms and Particle Swarm Optimization

Abstract

Accurate channel estimation is crucial for maintaining reliable wireless communication, particularly in 5G networks. Issues such as noise, interference, and fading can lead to errors that degrade system performance. This study investigates the use of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance channel estimation accuracy in noisy environments. Traditional methods like least squares (LS) and minimum mean square error (MMSE) often struggle under high noise and interference. GA and PSO offer ways to dynamically adjust important parameters, including pilot allocation, channel model parameters, and estimation weights, to ensure robust and adaptable estimation. GA, inspired by natural selection, evolves solutions to find optimal settings, while PSO, based on swarm intelligence, updates solutions through individual and collective experiences. The combination of GA and PSO improves accuracy by leveraging GA's global search capabilities with PSO's fast convergence. Simulation results demonstrate that this approach significantly reduces estimation errors, enhances signal-to-noise ratio (SNR), and increases spectral efficiency in 5G networks. This research adds to the field of machine learning-based signal processing by providing a novel method for optimizing channel estimation in noisy conditions. Future work will investigate the integration of deep learning with hybrid optimization models to further improve adaptability and computational efficiency.This paper tackles the challenge of improving wireless signal estimation in noisy and dynamic environments. Existing methods often struggle with noise, interference, and movement. We present a hybrid approach using Genetic Algorithm and Particle Swarm Optimization to dynamically optimize channel settings for better accuracy. Comprehensive analysis shows the GA-PSO hybrid consistently outperforms LS and MMSE estimators, achieving superior efficiency, fewer errors, and more reliable performance across various communication scenarios.