MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202621082571 A) filed by Lakshmi Narain College Of Technology & Science on July 04, 2026, for Agentic Ai-Based Dynamic Beamforming And Spectrum Management System For 5g Antenna Arrays.
Inventors include Prof. Niketan Mishra; Dr. Soheb Munir; Anuj Bhargava; Dr. Abhijeet Gupta; Prof. Sanket Choudhary; Dr. Aparna Gupta; Prof. Rahul Sharma; and Dr. Abhinav Bhargava.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: ABSTRACT Agentic AI-Based Dynamic Beamforming and Spectrum Management System for 5G Antenna Arrays The present disclosure relates to an Agentic AI-based dynamic beamforming and spectrum management system for 5G antenna arrays that autonomously optimizes wireless communication resources to enhance network performance and spectrum utilization. The proposed system comprises a multi-element 5G antenna array, a radio frequency transceiver, a channel state information acquisition module, an environmental sensing module, a spectrum sensing unit, a user mobility detection module, an Agentic AI controller, a beamforming optimization engine, a spectrum management engine, a reinforcement learning module, and a base station controller. These components collectively enable continuous monitoring of network conditions and intelligent adaptation of communication parameters. The Agentic AI controller incorporates multiple autonomous software agents configured to sense wireless network conditions, analyze communication data, predict channel quality and traffic demand, coordinate with neighboring base stations, and generate real-time decisions for beamforming and spectrum allocation. The beamforming optimization engine dynamically adjusts beam direction, beam width, antenna weighting coefficients, phase shifts, and transmission power, while the spectrum management engine intelligently allocates available frequency resources based on channel quality, interference levels, user mobility, traffic load, and service priority. The reinforcement learning module continuously updates optimization strategies using communication performance feedback to improve future decision-making. The proposed system enhances spectral efficiency, network throughput, communication reliability, and Quality of Service (QoS) while reducing latency, energy consumption, and inter-cell interference. By enabling autonomous collaboration among multiple AI agents and continuous adaptation to dynamic wireless environments, the invention provides an efficient, scalable, and intelligent communication framework suitable for 5G, Beyond 5G (B5G), and future 6G wireless communication networks, supporting applications such as smart cities, industrial IoT, autonomous transportation, and next-generation mobile broadband services.
Disclaimer: Curated by HT Syndication.
