Machine Learning-Enhanced Beamforming with Smart Antennas in Wireless Networks

Authors

  • Pavan Kumar Gade Software Developer, City National Bank, Los Angeles, CA, USA
  • Narayana Reddy Bommu Sridharlakshmi SAP Master Data Consultant, Data Solutions Inc., 28345 Beck Road, WIXOM, MI 48393, USA
  • Abhishekar Reddy Allam Software Developer, Compunnel Software Group Inc., Plainsboro, NJ, 08536, USA
  • Samuel Koehler Research Fellow, College of Engineering and Computer Science, University of Central Florida, USA

DOI:

https://doi.org/10.18034/abcjar.v10i2.770

Keywords:

Machine Learning, Beamforming, Smart Antennas, Wireless Networks, Signal Processing, Adaptive Algorithms, Channel Estimation, Reinforcement Learning

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.

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Published

2021-12-31

How to Cite

Gade, P. K., Sridharlakshmi, N. R. B., Allam, A. R., & Koehler, S. (2021). Machine Learning-Enhanced Beamforming with Smart Antennas in Wireless Networks. ABC Journal of Advanced Research, 10(2), 207-220. https://doi.org/10.18034/abcjar.v10i2.770

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