AI-Predicted QoS Coordination Between Access and Core Networks
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Solution Overview
Problem
The QoS parameters generated by the core network often fail to meet the service requirements on the access network side, leading to inefficiencies in service processing due to network latency and congestion.
Innovation Solution
An access network device uses an AI model to predict QoS association information, which is sent to the core network to determine a QoS parameter set that better aligns with the access network's requirements, allowing for efficient QoS parameter acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If QoS parameters are generated by the core network, then the QoS mechanism can be implemented, but the QoS parameters may not meet the service requirements on the access network side
Solution Approach 1:
The access network device performs AI model prediction in advance to generate QoS association information before the core network generates QoS parameters. This preliminary action ensures that the generated parameters are more likely to meet access network service requirements, avoiding rework and improving overall efficiency
Solution Approach 2:
The access network device sends QoS association information to the core network, creating a feedback loop that allows the core network to generate QoS parameters better aligned with access network requirements. This feedback mechanism resolves the contradiction by enabling continuous improvement of parameter suitability
2Reliability
If QoS parameters are delivered from core network to access network device, then the QoS mechanism can operate, but the parameters may not align with access network service requirements
Solution Approach 1:
The access network device prepares QoS association information using AI prediction before the core network generates parameters. This preliminary preparation reduces the time needed for parameter acquisition and iteration, while improving accuracy through informed prediction
Solution Approach 2:
The access network device autonomously generates QoS association information using its own AI model based on local service requirements. This self-service capability reduces dependency on core network parameter delivery and minimizes acquisition time
Data Source
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AI summary
This application discloses a communication method, apparatus, and system, a storage medium, and a program product. A core network element may obtain QoS association information predicted by an access network device, obtain a first QoS parameter set through prediction based on the QoS association information, and send the first QoS parameter set to the access network device. Because the first QoS parameter set is obtained based on the QoS association information predicted by the access network device, a service requirement on an access network side can be better met, so that the access network device can efficiently obtain an available QoS parameter, and efficiency of performing service processing by using the QoS parameter is improved.