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

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High-gain antenna arrays for millimetre-wave energy harvesting: architectures, challenges, and future directions Shalini Mirle Gajendra; Naveen Kalenahalli Bhoganna
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2896-2906

Abstract

The rapid expansion of fifth-generation (5G)/sixth-generation (6G) networks and internet of things (IoT) ecosystems has intensified the need for self sustaining power solutions to support billions of wireless devices. Millimetre-wave (mmWave) energy harvesting (EH) emerges as a viable alternative to traditional battery-powered systems, leveraging ambient radio frequency (RF) signals to provide continuous energy for IoT, smart sensor networks, and next-generation wireless applications. However, several challenges hinder its widespread adoption, including high path loss, low RF to-direct current (DC) conversion efficiency, and the trade-off between high gain and wide bandwidth. This paper presents a comprehensive review of high-gain mmWave antenna arrays, exploring state-of-the-art advancements in beamforming techniques, phased arrays, metasurface-enhanced rectennas, and multi-band EH architectures. We analyse existing methodologies, identifying key research gaps such as scalability constraints, material limitations, and real-world deployment challenges. Additionally, we highlight emerging trends, including artificial intelligence (AI)-driven adaptive beamforming, intelligent metasurfaces, and cost-effective fabrication techniques, which can significantly improve mmWave RF EH efficiency. By addressing these gaps, this study provides insights into future research directions for developing high-performance, scalable, and commercially viable mmWave EH solutions. The findings pave the way for the practical deployment of battery-free IoT devices, smart city infrastructures, and energy-autonomous wireless communication networks in the 6G era.
Multi-dimensional performance-optimized array design framework for efficient mmWave energy harvesting Shalini Mirle Gajendra; Naveen Kalenahalli Bhoganna
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 Naveen Kalenahalli Bhoganna