Network Communication Policy Feedback for AI Exception Detection
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Solution Overview
Problem
Current wireless communication systems face limitations in improving network performance due to each access network device independently determining policies based on AI models, leading to suboptimal execution and potential exceptions that affect overall network efficiency.
Innovation Solution
A communication method where network elements exchange information about policy execution statuses, allowing for real-time detection and correction of exceptions, enabling dynamic adjustment of policies through AI models or conventional methods to enhance network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If each access network device independently determines policy based on AI model, then device autonomy is improved, but network performance is worsened
Solution Approach 1:
The patent implements a feedback mechanism where access network devices report policy execution status and exceptions to the core network element. The core network element receives this feedback, analyzes the exceptions, and generates corrected policies or adjustments. This closed-loop feedback system allows individual devices to maintain autonomy while the central network element optimizes overall performance based on collected data, resolving the contradiction between device independence and network-wide performance.
2Productivity
If AI model is used for policy determination, then policy optimization is improved, but exception detection capability is worsened
Solution Approach 1:
The patent introduces the core network element as an intermediary between the AI model execution and exception detection. Access network devices execute AI-generated policies locally, but any exceptions or anomalies are detected and reported to the core network element. This intermediary collects exception data from multiple devices, analyzes patterns, and uses this information to refine future AI model outputs. This separates the optimization function (performed by AI at access devices) from the detection function (performed by core network element), resolving the contradiction.
3Productivity
If policy execution is monitored in real-time, then network performance is improved, but system complexity is worsened
Solution Approach 1:
The patent segments the monitoring system into two distinct parts: access network devices that perform simple local execution and status reporting, and a core network element that performs comprehensive analysis and policy adjustment. This segmentation allows real-time monitoring to be implemented without requiring complex systems at every access device. Each access device only needs to execute the AI policy and report basic status/exceptions, while the core network element handles the complex real-time analysis across the entire network, thus improving performance without significantly increasing complexity at the access level.
Data Source
AI summary
A communication method and apparatus are provided to improve network performance. A first network element obtains a to-be-input first measurement quantity and obtains a first artificial intelligence (AI) model. The first network element inputs the to-be-input first measurement quantity into the first AI model to obtain a first policy output by the first AI model and the first network element executes the first policy. When determining that an exception occurs when the first policy is executed, the first network element sends, to a second network element, indication information indicating that the exception occurs when the first policy is executed. A network element may perceive an execution status of executing, by another network element, a policy obtained based on an AI model so that network performance can be improved.