AI PDU Session Inactivity Timer Control for 5G Traffic Patterns
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing battery consumption and network resources due to varying traffic patterns and types, particularly in 5G systems supporting diverse services like URLLC and IoT, which require optimal network parameter application.
Innovation Solution
A method and apparatus utilizing an AI-based network data analytics function (NWDAF) to determine an inactivity timer value for protocol data unit (PDU) sessions by analyzing traffic patterns, types, and current time, optimizing battery consumption and network resource usage through deep reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed inactivity timer value is used for PDU sessions, then network resource management is simplified, but battery consumption efficiency deteriorates due to inability to adapt to varying traffic patterns
Solution Approach 1:
The inactivity timer value is transformed from a static fixed parameter to a dynamic variable that automatically adjusts based on real-time traffic pattern analysis. The system continuously monitors traffic characteristics and modifies the timer value accordingly, enabling adaptation to varying traffic conditions while maintaining relatively simple network resource management through automated decision-making.
Solution Approach 2:
The system implements a feedback mechanism where traffic pattern information is continuously collected, analyzed, and used to adjust the inactivity timer value. This closed-loop control allows the system to learn from observed traffic behaviors and optimize timer settings dynamically, improving battery efficiency without requiring complex manual configuration.
2Productivity
If the inactivity timer value is extended to maintain PDU session, then network resource usage efficiency is improved, but battery consumption increases due to prolonged session maintenance
Solution Approach 1:
The inactivity timer is made dynamic by adjusting its value based on analyzed traffic patterns. For active traffic patterns, the timer is extended to maintain PDU sessions longer, improving network resource efficiency. For inactive patterns, the timer is reduced to enable earlier session termination, conserving battery power. This dynamic adjustment resolves the contradiction between maintaining sessions and conserving energy.
Solution Approach 2:
The system changes the timer parameter value based on traffic pattern analysis results. By modifying this key parameter dynamically, the system optimizes the balance between network resource utilization and battery consumption, allowing PDU sessions to be maintained only when traffic patterns indicate active usage.
3Use of energy by moving object
If the inactivity timer value is reduced to save battery, then battery consumption efficiency is improved, but network resource usage efficiency deteriorates due to frequent session reconnections
Solution Approach 1:
The system dynamically adjusts the inactivity timer value based on real-time traffic pattern analysis. When traffic patterns indicate active usage, the timer is extended to reduce reconnections and improve network efficiency. When traffic is inactive, the timer is reduced to save battery. This dynamic behavior resolves the contradiction between battery efficiency and network resource efficiency.
Solution Approach 2:
The timer parameter is changed adaptively based on observed traffic characteristics. The system learns from traffic patterns and modifies the parameter to optimize both battery consumption and network resource usage, preventing frequent unnecessary reconnections while enabling timely session termination when appropriate.
4Productivity
If AI-based traffic pattern analysis is implemented, then inactivity timer optimization is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system implements self-service through automated AI-based traffic pattern analysis and autonomous timer value determination. The device independently analyzes its own traffic patterns and adjusts timer settings without external intervention, improving optimization effectiveness while managing complexity through self-contained processing.
Solution Approach 2:
The patent replaces traditional rule-based or manually configured timer setting mechanisms with AI-based analysis. This substitution enables more sophisticated optimization that adapts to complex traffic patterns, achieving better timer optimization while the AI processing is integrated into existing device infrastructure to manage complexity.
Data Source
AI summary
The present disclosure relates to a communication technique for converging IoT technology with a 5G communication system for supporting a higher data transmission rate beyond a 4G system, and a system therefor. The present disclosure may be applied to an intelligent service (for example, a smart home, a smart building, a smart city, a smart car or connected car, health care, digital education, retail business, a security and safety-related service, etc.) on the basis of 5G communication technology and IoT-related technology. A method carried out by a network data analytics function (NWDAF) device, and a device for carrying out same may be provided according to various embodiments of the present disclosure, the method comprising the steps of: acquiring, for a protocol data unit (PDU) session, traffic pattern-related information, traffic type-related information and current time-related information; determining an inactivity timer value for the PDU session on the basis of the traffic pattern-related information, traffic type-related information and current time-related information; and transmitting the inactivity timer value to a session management function (SMF) device.