volume | PIER Journals

Abstract

The exponential increase of data traffic in next generation wireless communication attracts optimized design of antenna arrays (AAs) to be deployed in RANs. The traditional antenna array synthesis techniques have become exhaustive leading to the introduction of machine learning assisted new binary optimization algorithm. In this paper, three specific AA features are given particular attention: peak sidelobe level (PSLL), first null beam width (FNBW), and broad sector null in interference directions. These contrast each other, and a multi-objective new binary cat swarm optimization (MO-NBCSO) with a novel mutation probability is developed to derive the best-compromised solutions among them. The computational complexity is approximated as O(MN²) (here, M and N represent the number of objectives and population size, respectively). Hence, a 20×20 planar antenna array is considered for synthesis and pareto fronts are generated alongside state-of-the-art MO algorithms. A fuzzy-based decision approach is introduced to choose the best trade-off solutions. A detailed comparative performance study is carried out by the two-performance metrics, namely, I-metric and S-metric. Numerical results illustrate that MO-NBCSO is a better candidate to produce the best antenna arrays in terms of array characteristics over other algorithms.

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Ms. Sahiti Vankayalapati

Dr. Lakshman Pappula