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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mirle Gajendra, Shalini
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Computer Science & IT Engineering

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Multi-dimensional performance-optimized array design framework for efficient mmWave energy harvesting Mirle Gajendra, Shalini; Kalenahalli Bhoganna, Naveen
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1143-1154

Abstract

The proliferation of next-generation wireless networks and autonomous devices has intensified the need for efficient and compact energy harvesting solutions at millimeter-wave (mmWave) frequencies. This paper presents a multi-dimensional performance-optimized array design framework for mmWave energy harvesting (MAPLE-H), which enables the systematic development of advanced antenna arrays that fulfill the simultaneous demands of wide operational bandwidth, high efficiency, polarization diversity, and miniaturization. The proposed framework integrates simulation-driven array modeling with integrated analog–digital beamforming and adaptive entity partitioning, accommodating real-world energy harvesting array non-idealities. Furthermore, an energy–information optimization factor is introduced to dynamically balance the trade-off between energy harvesting and data communication performance. Intelligent energy–information resource optimization algorithms jointly tune design parameters to maximize harvested power and signal integrity across diverse deployment scenarios. Comprehensive simulation results and comparative benchmarking demonstrate that the proposed framework consistently outperforms state-of-the-art designs in terms of gain, bandwidth, robustness, and flexibility, positioning it as an enabling technology for future energy autonomous wireless systems.
Co-Authors Kalenahalli Bhoganna, Naveen