Comparison between LC-LMS and LC-RLS Algorithms for Improving Adaptive Beamforming

Adaptive filtering is a crucial technology that enables interference suppression and enhances signal quality. This research presents a sophisticated adaptive beamforming method that minimises the Mean Squared Error (MSE) and significantly increases convergence rates by employing the Recursive Least Squares (RLS) algorithm. Utilising the RLS algorithm, which is renowned for its quick convergence and resilience to changing signal conditions, the antenna array's weights are dynamically modified. The suggested approach outperforms more established algorithms like Least Mean Squares (LMS) in terms of quicker convergence and lower MSE, as demonstrated by comprehensive simulations and real-world applications. The findings demonstrate the significant efficacy of the RLS-based adaptive beamforming technology in real-time applications, providing improved signal clarity and reliability in challenging and noisy environments. The potential of RLS to develop adaptive beamforming technologies and open the door to more dependable and effective systems for communication is highlighted in this work.