MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118326 A) filed by Dr. M. Shoukath Ali; Dr. R. Suganya; Sowmiya G; Bethi Saiteja; Mrs. D. Sasikala; Kailash Karunakaran; Mohamed Sameer T K; S. Jeyapriyanga; Vijesh Malappurath Paramadam; Mrs. N. Anandha Priya; Salik Ram Dewangan; and Nithya D on October 02, 2026, for Deep Learning-Based Intelligent 6g Communication Framework For Real-Time Autonomous Vehicle Networks.
Inventors include Dr. M. Shoukath Ali; Dr. R. Suganya; Sowmiya G; Bethi Saiteja; Mrs. D. Sasikala; Kailash Karunakaran; Mohamed Sameer T K; S. Jeyapriyanga; Vijesh Malappurath Paramadam; Mrs. N. Anandha Priya; Salik Ram Dewangan; and Nithya D.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: The invention pertains to an intelligent 6G communication framework based on deep learning for autonomous vehicles communication networks in real time. The invention includes the following components: autonomous vehicle communication units, roadside communication units, edge computing nodes, a network controller, a communication-intent processing unit, a deep learning prediction engine, an intelligent resource-management engine, a predictive mobility and handover unit, a safety- aware information prioritization unit, and a feedback unit. The network receives vehicle state information, sensor information, mobility information, communication state information, application requirements, and network conditions. A communication-intent processing unit creates machine-readable representations of communication requirements and safety priority. The intelligent resource management engine pre-actively allocates the spectrum, transmission power, direction of beams, routing, network node affiliation, and network slice resources. Predictive handover is carried out based on the prediction of future position and the state of the communication nodes. The edge computing module co-decides on the requirements of communication and computing resources for the autonomous driving application. Safety-aware information prioritization ensures that critical information is given priority in communication. Either distributed or federated learning algorithms may be used for adaptive model updating. The predictive communication risk engine takes preventive action in the network based on future network degradations. Communication performance feedback is provided to the learning engine. FIG.1.
Disclaimer: Curated by HT Syndication.
