Ayshwarya B.; R. Naveenkumar; S. Prabu; E. Sabitha; Arun M.; B. Girirajan

ML-Assisted Joint Optimization of Microwave Components and Wireless Architectures via Adaptive Pelican Optimization

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Author(s)

Ayshwarya B. ¹ R. Naveenkumar ^2,* S. Prabu ³ E. Sabitha ⁴ Arun M. ⁵ B. Girirajan ⁶

1. Department of Computer Science, School of Computational and Physical Sciences, Kristu Jayanti (Deemed to be University), Bengaluru - 560077, Karnataka, India

2. CGC University Mohali, Dept of CSE, Punjab, 140307, India

3. Department of ECE, Mahendra Institute of Technology, Namakkal - 636106, India

4. Department of Computer Science and Business Systems, Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi, Chennai – 600062, India

5. Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai – 600123, Tamil Nadu, India

6. School of Computer Science and Artificial Intelligence, SR University, Warangal – 506371, Telangana, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijwmt.2026.05.20

Received: 24 Jul. 2026 / Revised: 15 Aug. 2026 / Accepted: 24 Sep. 2026 / Published: 8 Oct. 2026

Index Terms

Machine Learning, Pelican Optimization Algorithm, Microwave Components, Wireless Architectures, Artificial Intelligence, RF Systems, Beyond-6G Communication, Microwave Antenna Optimization, Beamforming, Intelligent Wireless Networks

Abstract

The rapid evolution of next-generation wireless communication systems has increased the demand for intelligent microwave components and energy-efficient wireless architectures capable of supporting ultra-high data rates, low latency, massive device connectivity, and adaptive resource management. Conventional optimization approaches often experience slow convergence, high computational complexity, and limited adaptability when optimizing multiple communication parameters simultaneously. To address these challenges, this paper proposes a Machine Learning-Assisted Adaptive Pelican Optimization (ML-APO) framework for the joint optimization of microwave components and wireless communication architectures. The proposed framework integrates an Adaptive Pelican Optimization Algorithm with a machine learning-assisted surrogate prediction model that rapidly estimates communication performance metrics, thereby significantly reducing computational overhead during the optimization process. The optimization simultaneously considers transmission power, antenna parameters, microwave component characteristics, spectrum allocation, routing efficiency, and communication reliability through a multi-objective fitness function. The adaptive search strategy of the Pelican Optimization Algorithm effectively balances global exploration and local exploitation, enabling faster convergence toward optimal network configurations while avoiding premature convergence. Extensive simulation results demonstrate that the proposed ML-APO framework achieves 96.41% energy efficiency, 43.8% improvement in network lifetime, 13.56 Gbps throughput, 99.43% packet delivery ratio, 0.49 ms end-to-end latency, and 0.54% packet loss, while reducing optimization time by 35.8% and improving convergence speed by 39.6% compared with existing optimization methods. These results demonstrate that the proposed framework provides an efficient, scalable, and intelligent solution for designing high-performance microwave components and next-generation wireless communication architectures suitable for future 6G and beyond wireless networks.

Cite This Paper

Ayshwarya B., R. Naveenkumar, S. Prabu, E. Sabitha, Arun M., B. Girirajan, "ML-Assisted Joint Optimization of Microwave Components and Wireless Architectures via Adaptive Pelican Optimization", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.5, pp. 330-347, 2026. DOI:10.5815/ijwmt.2026.05.20

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International Journal of Wireless and Microwave Technologies (IJWMT)