MUMBAI, India, Aug. 31 -- Intellectual Property India has published a patent application (202621082289 A) filed by Mrs. S. Mohana Priya; Mrs. R K Ananthi; D. Karunkuzhali; Ms. Praveena. V; Dr. Shobana D; Dr. Ghouse Ahamed Z; Dr. Komala K; and Dr. S Anto Martin Pradeep on July 03, 2026, for 6g-Enabled Intelligent Communication Framework Using Ai For Ultra-Reliable Connectivity And Adaptive Network Optimization.
Inventors include Mrs. S. Mohana Priya; Mrs. R K Ananthi; D. Karunkuzhali; Ms. Praveena. V; Dr. Shobana D; Dr. Ghouse Ahamed Z; Dr. Komala K; and Dr. S Anto Martin Pradeep.
The application for the patent was published on August 28, 2026, under issue no. 35/2026.
Abstract: 6G-Enabled Intelligent Communication Framework Using AI for Ultra-Reliable Connectivity and Adaptive Network Optimization Abstract The present invention relates to the field of next-generation wireless telecommunication and discloses an intelligent communication framework for sixth-generation networks. This framework utilizes artificial intelligence methods to ensure ultra- reliable communication and provide opportunities for adaptable network resource and performance optimization. Sixth-generation wireless networks are designed to support critical applications that demand high levels of reliability, low-latency data transfer, and extensive connectivity. However, the management of such networks is a challenging task due to their complex structure, a large number of connected devices, varying channel conditions, and heterogeneous quality of service requirements. Prior art solutions based on static configurations and reactionary approaches often experience difficulties in efficiently allocating resources and maintaining stable communication when faced with dynamic and unpredictable conditions. The framework of the invention addresses this and other issues found in the art by introducing artificial intelligence to handle network optimization in real-time. It processes telemetry data from the network and connected devices to predict failures and implement proactive resource management decisions using prediction models. Reinforcement learning methods are used to train and update policies that optimize spectrum allocation, power control, beamforming, and routing decisions. The framework follows a distributed approach that enables independent network entities to cooperate and optimize performance while preserving data privacy. The framework consists of layers that perform sensing, processing, and execution of decisions, allowing networks to self-optimize and self-heal with minimal human intervention. These capabilities improve the networks’ ability to adapt to emerging conditions and demands while reducing the operational costs associated with traditional networks. The invention’s methods and devices facilitate connection reliability, low-latency communication for time-critical applications, efficient spectrum and power usage, and the realization of emerging use cases that require ultra-reliable communication networks. These benefits enable improved quality of experience for the end-users, reduced operational costs for network operators, and the development of new technology-enabled services that were previously infeasible. The detailed description of embodiments and examples is provided in the specification and drawings, while specific claims define the protection scope of the invention.
Disclaimer: Curated by HT Syndication.
