Multi-RAT Coordination Frameworks in 6G Wireless Architectures
Vinay Kumar Sadolalu BoregowdaAssistant Professor, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Ramanagara District, Karnataka sb.vinaykumar@jainuniversity.ac.in0000-0001-7349-1697
Dr. Bharat Jyoti Ranjan SahuAssociate Professor, Centre for Cyber Security, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha bharatjyotisahu@soa.ac.in0000-0002-3487-2822
Dr. Raja Jitendra NayakaAssistant Professor, Department of Computer Science Engineering, Presidency University, Bangaluru, Karnataka raja.jitendra@presidencyuniversity.in0000-0002-4127-1670
Dhajvir Singh RaiSchool of Engineering & Computing, Dev Bhoomi Uttarakhand University pc.cse1@dbuu.ac.in0009-0008-2736-031X
Sorabh SharmaCentre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab sorabh.sharma.orp@chitkara.edu.in0009-0000-1805-0418
Bhavya VinilAssistant Professor, MBA Department, Presidency College, Bengaluru, Karnataka bhavya.vinil@presidency.edu.in0000-0002-9722-5542
Coordination structures of various Multi -RATs in wireless 6G architectures enable the seamless integration of heterogeneous radio access technologies (Multi -RATs), such as sub-combination, MMWAE, and Wi-Fi 7. These return structures ensure resource efficiency, resource reliability, dynamic conference, low-latency experience, and intense efficiency. The objectives of the coordination structures of various Multi -RAT in 6G wireless architectures include obtaining perfect interoperability between various radio access technologies, optimizing spectrum use, minimizing latency during transfers, improving energy efficiency, and supporting low energy-related communications (URLLC). These structures aim to ensure intelligent connectivity on heterogeneous networks for next-generation applications. To implement coordination structures of various Multi -RAT in 6G, methods such as AI-oriented resource allocation, software-defined network (SDN), and network function virtualization (NFV) are employed. They allow real-time decision making, dynamic rat selection, and smart traffic direction. Simulation models and test shards integrating sub-THz, MMWAE, and legacy systems are designed to validate performance. The results show a 35% improvement in spectral efficiency, 40% reduction in delivery latency, and higher service quality for high mobility scenarios. Coordinated architecture also demonstrated better adaptability to network conditions, ensuring perfect connectivity and reliability in various cases of use, including IoT, AR/VR, and autonomous systems.