Machine Learning-Enhanced Beamforming with Smart Antennas in Wireless Networks

Abstract

This research integrates machine learning (ML) approaches into beamforming using smart antennas to improve wireless networks. The main goals are to evaluate ML-driven beamforming techniques for enhancing SNR, BER, and throughput while tackling dynamic environments and interference. The study synthesizes simulation and experimental results using secondary data. Significant results show that ML-enhanced beamforming outperforms standard approaches by improving SNR by 15 dB, lowering BER by 30-50%, and decreasing interference. However, sophisticated ML algorithms are computationally demanding and need high-quality training data. Policy implications emphasize the need for effective data governance frameworks to assure data integrity, security, and efficient algorithms that can function within infrastructure restrictions. Stakeholders should collaborate to create standardized methods that optimize the advantages of ML-enhanced beamforming while addressing concerns, opening the door for more intelligent, more adaptable wireless communication systems.