Designing Smart Antennas Using Machine Learning Algorithms


  • Barsa Samantaray SOA University, Bhubaneswar, India
  • Kunal Kumar Das SOA University, Bhubaneswar, India
  • Jibendu Sekhar Roy KIIT University, Bhubaneswar, Odisha, India



artificial neural network, decision tree, ensemble algorithm, machine learning, smart antenna, support vector machine


Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.


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Author Biographies

Barsa Samantaray, SOA University, Bhubaneswar, India

Assistant Professor, ECE Department, ITER, SOA University, Bhubaneswar, India

Kunal Kumar Das, SOA University, Bhubaneswar, India

Associate Professor, ECE Department, ITER, SOA University, Bhubaneswar, India


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How to Cite

Samantaray, B., Das, K. K., & Roy, J. S. (2023). Designing Smart Antennas Using Machine Learning Algorithms. Journal of Telecommunications and Information Technology, 4(4), 46–52.




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