AI Network Orchestration for Policy-Driven QoS and Resource Allocation
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Solution Overview
Problem
The increasing complexity and diversity of wireless networks, particularly with the advent of 6G, pose challenges in managing network resources efficiently due to the variety of devices and data demands, necessitating improved network management and orchestration.
Innovation Solution
The integration of artificial intelligence (AI) in wireless networks to optimize resource allocation, network functions, and enhance communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional network management methods are used to manage wireless networks, then network operations can be maintained, but network complexity and difficulty of management increase with the advent of 6G and diverse devices
Solution Approach 1:
The patent implements self-service through autonomous network management systems that automatically perform configuration, optimization, and troubleshooting without human intervention. AI algorithms analyze network states and execute management decisions autonomously, reducing operational difficulty despite increasing network complexity
Solution Approach 2:
The patent replaces traditional mechanical/manual network management systems with AI-based intelligent systems. Machine learning models and automated algorithms substitute human operators and manual processes, enabling efficient management of complex 6G networks through intelligent decision-making rather than conventional methods
2Adaptability or versatility
If network resources are allocated to support diverse devices and applications, then network versatility increases, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements dynamic resource allocation where network resources are continuously adjusted based on real-time device requirements and network conditions. AI algorithms dynamically allocate bandwidth, computing resources, and energy according to actual demand, enabling the network to adapt to diverse devices while maintaining high resource utilization efficiency through flexible, context-aware management
3Reliability
If more network functions are added to support 6G requirements, then network capability improves, but system complexity increases
Solution Approach 1:
The patent merges multiple network functions into integrated AI-based management systems. Rather than managing separate functions independently, the system combines configuration management, resource allocation, security, and optimization into unified intelligent platforms, reducing system complexity while maintaining enhanced 6G capabilities through coordinated multi-functional operations
Data Source
AI summary
An apparatus and system to enable MNO policy-driven AI decisions and frame structure are described. An AI SAP receives modified context information within a network and determines a response to network events based on MNO policies. The AI SAP includes a context-aware management entity that tracks and updates the context information, a cognition framework entity that processes new data, applies inferences and compares results of the inferences to available knowledge, a situational awareness entity that determines effects of events within the system on objectives based on the MNO policies, and a policy management entity that provides behavioral rules on the system based on the MNO policies. The AI SAP provides QoS modifications based on positional and movement information of a UE by determining timing indicating when the UE is to be within the AP range and adjusting AP activation and synchronization signaling for the UE based on the timing.