AI-Based Timer Control for Adaptive UE Communication States
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Solution Overview
Problem
Current wireless communication systems fail to adaptively adjust timer durations based on user equipment (UE) behavioral characteristics, leading to suboptimal performance in rapidly changing environments, which can result in increased service interruption times and unnecessary resource contention.
Innovation Solution
A timer control method that employs artificial intelligence (AI) to adaptively adjust timer durations by combining UE-collected information with network-provided duration-related information, allowing the UE to dynamically adjust timers based on its state and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If timer durations are configured by network side via dedicated signaling, then timer control is centralized and manageable, but the system cannot adaptively adjust timers based on UE behavioral characteristics
Solution Approach 1:
The UE autonomously determines timer durations by collecting behavioral characteristics data and applying AI models locally, without requiring network side configuration or control. This self-service approach enables adaptive timer adjustment based on actual UE behavior patterns while reducing network signaling overhead and control complexity.
Solution Approach 2:
The system changes the timer duration parameter dynamically based on UE behavioral characteristics. By using AI models to analyze UE behavior patterns (such as data transmission intervals, mobility patterns, and application types), the timer duration is adjusted to match actual usage patterns, improving adaptability without requiring complex network control mechanisms.
2Loss of time
If fixed timer durations are used, then implementation is simple, but service interruption time increases in rapidly changing environments
Solution Approach 1:
The system implements feedback by continuously monitoring UE behavioral characteristics and using this information to adjust timer durations. The AI model analyzes ongoing UE behavior patterns and provides feedback signals that modify timer settings in real-time, reducing service interruption time by adapting to environmental changes while maintaining simple implementation through automated decision-making.
Solution Approach 2:
The timer duration transitions from a static fixed value to a dynamic parameter that changes based on UE behavioral characteristics. The system uses AI models to continuously update timer settings according to real-time UE behavior patterns, enabling the timer to adapt to rapidly changing environments and reduce service interruption time without complex manual configuration.
3Object-affected harmful factors
If timer durations are extended to prevent contention conflicts, then resource contention is reduced, but access latency increases
Solution Approach 1:
The system dynamically changes timer duration parameters based on UE behavioral characteristics and current network conditions. By using AI models to predict optimal timer values, the system adjusts timer durations to be sufficiently long to prevent resource contention but not excessively long to cause access latency, achieving a balanced optimization based on actual usage patterns.
Data Source
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AI summary
The present application relates to a timer control method and a communication device. The method comprises: a first communication device determines control information of a timer on the basis of duration related information of the timer and state information of the first communication device. According to the embodiments of the present application, the first communication device can determine the control information of the timer on the basis of the duration related information of the timer and the state information of the first communication device, so that the timer can be adaptively adjusted, and the performance of the communication device is improved.