MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641082981 A) filed by Geetha G; Rekha G Nair; and Gireesh V Puthusserry on July 06, 2026, for Machine Learning Based Reconfigurable Mimo Antenna With Dynamic Frequency And Polarization Control.
Inventors include Geetha G; Rekha G Nair; and Gireesh V Puthusserry.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: Title of Invention Machine Learning Based Reconfigurable MIMO Antenna with Dynamic Frequency and Polarization Control ABSTRACT: The present invention discloses a machine learning-based reconfigurable Multiple-Input Multiple-Output (MIMO) antenna with dynamic frequency and polarization control for next-generation wireless communication systems. The invention integrates a reconfigurable MIMO antenna array with a machine learning decision engine, an environmental sensing module, a frequency reconfiguration controller, a polarization control unit, an RF switching network, and a communication processor to enable intelligent and autonomous adaptation of antenna operating characteristics. The sensing module continuously acquires real-time communication parameters, including signal strength, signal-to-noise ratio, channel state information, interference level, spectrum availability, traffic load, and user mobility. The acquired data are analyzed using trained machine learning algorithms to predict the optimal antenna configuration. Based on the prediction, the system dynamically adjusts the operating frequency, polarization mode, and antenna switching configuration to maximize communication throughput, improve spectral efficiency, reduce interference, minimize polarization mismatch, and enhance energy efficiency. The invention supports automatic adaptation across multiple wireless standards, including 5G, Beyond 5G (B5G), 6G, Internet of Things (IoT), cognitive radio, satellite communication, and unmanned aerial vehicle (UAV) networks. The closed-loop adaptive learning mechanism continuously updates the prediction model using real- time communication feedback, enabling improved decision accuracy and robust wireless performance under dynamic operating conditions. The proposed invention provides an intelligent, scalable, and self-optimizing antenna system that significantly enhances communication reliability, spectrum utilization, and overall network performance compared with conventional fixed and rule-based reconfigurable antenna systems.
Disclaimer: Curated by HT Syndication.
