MUMBAI, India, Aug. 24 -- Intellectual Property India has published a patent application (202641098440 A) filed by Ms. Maheswari; Dr. R. Akila; Ms. R. Mahalakshmi; Dr. A. Elakkiya; Mrs. R. Rajakumari; Dr. Shobana D; Mrs A. Sumathi; and Dr. Jayanthi. E on August 14, 2026, for Machine Learning-Enabled Adaptive Smart Antenna And Rf Beamforming System For High-Speed Next-Generation Wireless Communication Networks.
Inventors include Ms. Maheswari; Dr. R. Akila; Ms. R. Mahalakshmi; Dr. A. Elakkiya; Mrs. R. Rajakumari; Dr. Shobana D; Mrs A. Sumathi; and Dr. Jayanthi. E.
The application for the patent was published on August 21, 2026, under issue no. 34/2026.
Abstract: The current invention relates to a machine learning based adaptive smart antenna and radio frequency beamforming system for high speed next generation wireless communication networks. The system includes a multi-element electronically steered antenna array, RF front end, signal-processing subsystem, machine learning controller, beam shaping controller and closed loop feedback system. The wireless-channel information and environmental information, such as channel-state information, received signal strength, signal-to-interference-plus-noise ratio, interference characteristics, user mobility, Doppler information, historical beam states and communication performance are processed to generate an adaptive control representation. The representation is processed by a machine-learning controller to simultaneously identify beam direction and one or more antenna attributes including element phase, element amplitude, impedance or tuning state, beam breadth, beam shape and interference-null position. The needed features are realized on the antenna array by using the electronical adjustable RF circuits. Then, the derived communication performance is assessed thru: throughput, SINR, received signal intensity, error rate, delay, interference level, beam alignment or energy related metrics. The measured performance is used to further personalize the machine-learning driven antenna state. A mobility prediction system is able to predict future communication situations and provide predictive beam tracking. A reinforcement learning algorithm can be used to further optimize antenna topologies using communication performance as the incentive. The system can be built on edge, centralized, distributed or federated machine learning and can handle single-user and multi-user communication. The innovation can be used in cellular, massive-MIMO, millimeter-wave, terahertz, vehicle, satellite, wireless-backhaul, and Internet-of- things communication systems.
Disclaimer: Curated by HT Syndication.
