IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 15, No 3: June 2026

High-gain antenna arrays for millimetre-wave energy harvesting: architectures, challenges, and future directions

Shalini Mirle Gajendra (Adichunchanagiri University)
Naveen Kalenahalli Bhoganna (Adichunchanagiri University)



Article Info

Publish Date
01 Jun 2026

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.

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Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...