Description:FIELD OF INVENTION
The main field of invention is Telecommunications Engineering, Artificial Intelligence, 6G Networks, Network Management, Optimization, Machine Learning, Edge Computing, SDN, NFV, IoT, QoS, Automation, Security, Data Analytics, Cloud Computing, Wireless Communication, Self-Healing Networks, Resource Allocation, Smart Connectivity, Energy Efficiency.
BACKGROUND OF INVENTION
The rapid evolution of wireless communication technologies has led to the development of 6G, promising ultra-low latency, high data rates, and intelligent connectivity. However, managing 6G networks presents challenges such as dynamic resource allocation, energy efficiency, and real-time decision-making. Traditional network management methodologies rely on rule-based and heuristic approaches, which struggle to handle the complexity and scalability of 6G infrastructures.
Existing methodologies include Software-Defined Networking (SDN) for flexible control, Network Function Virtualization (NFV) for efficient resource utilization, and edge computing for reduced latency. Machine learning (ML) and artificial intelligence (AI) have been integrated into network management to improve predictive analytics and automate network optimization. However, current AI implementations often lack adaptability and real-time responsiveness to dynamic network conditions.
The proposed AI-optimized network management platform leverages deep learning, reinforcement learning, and self-healing mechanisms to enhance network efficiency, optimize resource allocation, and ensure seamless connectivity in 6G environments.
the patent application number 202141007761 discloses a design and architecture of uav system for 6g cellular system. UAV-assisted 6g system integrates ai, edge computing, beamforming, MIMO, blockchain, SDN, NFV, IOT, energy efficiency, security, and autonomy.
the patent application number 202141034578 discloses a systems and method of improved resources sharing in 5g/6g wireless system. ai-driven dynamic resource allocation, network slicing, edge computing, SDN, NFV, QOS optimization, latency reduction, spectrum efficiency, load balancing, smart connectivity.
SUMMARY
The AI-Optimized Network Management Platform is designed to enhance the performance, reliability, and efficiency of 6G telecommunications infrastructure. By leveraging artificial intelligence, machine learning, and real-time data analytics, the platform autonomously optimizes network resource allocation, reduces latency, and ensures seamless connectivity. It integrates key technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing to provide intelligent decision-making and self-healing capabilities. The system continuously monitors network conditions, predicts potential failures, and dynamically adjusts parameters to enhance Quality of Service (QoS) and Quality of Experience (QoE).
Objective of the Invention
The primary objective of this invention is to develop an AI-driven network management framework that improves 6G network performance through intelligent automation. It aims to reduce network congestion, optimize resource utilization, enhance energy efficiency, and provide a self-adaptive, scalable, and secure communication environment for next-generation wireless technologies.

DETAILED DESCRIPTION OF INVENTION
The AI-Driven Evolution of 6G Networks
The integration of Artificial Intelligence (AI) into 6G networks is not just an upgrade—it represents a fundamental shift in how networks operate. As AI continues to advance, it is reshaping telecommunications by enabling self-optimizing, autonomous systems that enhance network performance, security, and user experience. With 6G expected to support ultra-fast speeds, ultra-low latency, and massive device connectivity, AI will be crucial in managing the complexity of these networks efficiently.

Figure 1: Architecture of AI enabled 6G network
AI’s Impact on 6G Network Architecture
Traditional network management techniques rely on static configurations and reactive maintenance, often struggling to handle the dynamic nature of modern telecommunications. AI introduces a more adaptive and predictive approach to network management.
• Predictive Maintenance & Self-Healing Networks
AI-driven analytics can detect early signs of potential network failures and proactively take corrective measures before they impact users. This reduces downtime and improves reliability.
• Dynamic Resource Allocation
AI continuously analyzes real-time network data, identifying congestion points and automatically redistributing resources to maintain optimal performance. Machine learning algorithms allow networks to anticipate traffic patterns and adjust bandwidth allocation dynamically.
• Enhanced Decision-Making
Unlike traditional rule-based systems, AI enables networks to make intelligent, data-driven decisions, reducing human intervention and optimizing network operations. Ericsson’s research indicates that AI-powered 6G networks could improve efficiency by up to 40%, surpassing conventional network management strategies.
Revolutionizing Resource Management with AI
One of the biggest challenges in 6G networks is efficiently managing resources to accommodate high traffic loads, increasing device density, and evolving user demands. AI transforms resource management through:
• Smart Bandwidth Allocation
AI-powered systems continuously monitor network usage and allocate bandwidth based on demand. Unlike static allocation methods, AI enables networks to redistribute resources in real time, ensuring optimal performance even during peak hours.
• Network Slicing for Customization
AI facilitates intelligent network slicing, allowing telecom providers to create multiple virtual networks tailored to different use cases. For example, an ultra-reliable low-latency (URLLC) slice can be optimized for remote surgery, while a massive IoT slice can support large-scale sensor networks.
• Energy Efficiency in 6G
AI improves energy efficiency by optimizing power usage based on network demand. Studies show that AI-driven resource management can reduce energy consumption by up to 25% during peak periods, contributing to greener, more sustainable telecommunications infrastructure.
AI-Enhanced Security in 6G Networks
Security is a major concern in next-generation networks, with 6G expected to support highly sensitive applications such as autonomous driving, smart cities, and remote healthcare. AI plays a pivotal role in strengthening network security through:
• Real-Time Threat Detection & Prevention
AI-powered security systems analyze network traffic patterns and detect anomalies that could indicate cyber threats. By identifying malicious activities in real time, AI prevents attacks before they can cause harm.
• Automated Incident Response
Traditional security measures often rely on manual intervention to address threats. AI enables automated responses to security incidents, reducing response times by up to 60% and minimizing potential damage.
• Adaptive Cybersecurity Frameworks
AI-driven security frameworks continuously evolve by learning from new threats. These systems adapt to emerging cybersecurity challenges, ensuring robust protection against sophisticated attacks in 6G networks.
Optimizing User Experience with AI
AI enhances the overall user experience in 6G networks by proactively identifying potential disruptions and maintaining high service quality. Key contributions include:
• Service Quality Prediction & Optimization
AI-powered models analyze user behavior patterns and network performance indicators to anticipate service disruptions. By making real-time adjustments, AI ensures seamless connectivity and minimizes latency.
• Personalized Network Services
AI enables telecom providers to offer personalized services based on user preferences and usage patterns. This enhances customer satisfaction and provides tailored experiences for different applications, such as ultra-HD streaming and cloud gaming.
• Reduction in Service Interruptions
AI-driven automation has been shown to reduce service interruptions by 45%, ensuring consistent network availability and reliability for users.
Business Opportunities in AI-Enhanced 6G
The fusion of AI and 6G creates new opportunities for businesses and start-ups. Companies can explore:
• AI-Driven Network Optimization Platforms
Businesses can develop AI-powered platforms that provide predictive maintenance, real-time performance monitoring, and network optimization services for telecom operators.
• Industry-Specific AI Solutions
Start-ups can design AI-based solutions for specialized industries. For example, ultra-low latency AI models can enhance autonomous vehicle communication, while AI-optimized healthcare networks can support remote robotic surgeries.
• AI-Powered Security Solutions
With cybersecurity becoming a top priority in 6G, AI-driven security services will be in high demand. Real-time threat detection and automated mitigation mechanisms can offer telecom operators a robust defense against cyberattacks.
Conclusion
The integration of AI into 6G telecommunications marks a transformative shift in how networks operate, ensuring enhanced performance, security, and efficiency. AI-driven predictive maintenance, dynamic resource allocation, and real-time security measures redefine traditional network management. As AI continues to evolve, 6G networks will become self-optimizing, enabling ultra-reliable, ultra-fast, and energy-efficient telecommunications. This convergence not only enhances user experience but also unlocks new business models, paving the way for a smarter, AI-powered future in global connectivity.

DETAILED DESCRIPTION OF DIAGRAM
Figure 1: Architecture of AI enabled 6G network , Claims:1. AI-Optimized Network Management Platform for Enhanced Performance in 6G Telecommunications Infrastructure claims that an AI-driven network management platform that enhances 6G telecommunications infrastructure by optimizing resource allocation, predictive maintenance, and dynamic routing.
2. A machine learning-based framework that analyzes real-time network traffic to anticipate congestion and autonomously adjust bandwidth distribution for ultra-low latency.
3. A reinforcement learning (RL)-enabled mechanism for continuous self-optimization of network parameters, ensuring adaptive response to changing traffic conditions.
4. A predictive analytics system leveraging Speed-Optimized Long Short-Term Memory (SP-LSTM) networks to forecast network demand and proactively prevent service disruptions.
5. An AI-powered cybersecurity module that detects and neutralizes network threats in real-time using anomaly detection and threat mitigation algorithms.
6. An energy-efficient network optimization model that reduces power consumption and enhances sustainability in 6G network operations.
7. A user experience enhancement system that utilizes AI-driven quality of service (QoS) monitoring and automated troubleshooting to minimize service interruptions.
8. A self-learning network architecture that continuously refines its operational strategies based on historical data and evolving network trends.
9. An AI-enhanced load balancing technique that ensures optimal distribution of computational resources across network nodes for improved performance.
10. A scalable and modular AI-based network management framework designed to seamlessly integrate with future advancements in 6G infrastructure.