AI-Based DRX Cycle Control for Low-Delay 5G Power Saving
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Solution Overview
Problem
Traditional DRX mechanisms in 5G networks set fixed sleep durations that do not adapt to changing data packet arrival times, leading to inefficiencies in energy consumption and potential delays.
Innovation Solution
Employing AI models, specifically LSTM networks, to predict data packet arrival times and dynamically adjust DRX cycles based on service types, ensuring terminals wake up before data arrival and minimize energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed sleep duration is set by traditional DRX mechanism, then the implementation is simple, but the data transmission delay is large
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed sleep duration to a dynamic DRX cycle that is continuously adjusted based on AI predictions of data packet arrival times. The network device monitors actual packet arrivals and updates the DRX cycle parameters to optimize both energy savings and transmission timing, resolving the contradiction between simple implementation and adaptive performance.
Solution Approach 2:
The patent implements feedback mechanisms where the network device receives feedback information about actual data packet arrival times and uses this information to adjust the DRX cycle. This closed-loop feedback allows the system to learn from actual behavior and refine its predictions, reducing transmission delays while maintaining implementation simplicity through automated adjustments.
2Use of energy by moving object
If a long short-term sleep cycle is configured to reduce energy consumption, then energy consumption is reduced, but data transmission delay increases
Solution Approach 1:
The patent applies preliminary action by using AI models to predict future data packet arrival times before the terminal actually needs to wake up. The system proactively adjusts the DRX cycle based on these predictions, allowing the terminal to sleep longer without causing delays, as the wake-up time is predetermined by the AI model's arrival time prediction rather than reactive to actual packet arrival.
Solution Approach 2:
The patent changes the parameter of DRX cycle duration dynamically based on AI predictions. Instead of using a fixed long sleep cycle that causes delays, the system adjusts the cycle length according to predicted packet arrival patterns, optimizing the balance between energy consumption and transmission timing by making the parameter adaptive rather than static.
3Use of energy by moving object
If AI model is used to predict data packet arrival time, then energy consumption is reduced, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary approach by having the network device perform the AI prediction calculations and then communicate the optimized DRX cycle parameters back to the terminal. This intermediary role of the network device handles the computational complexity of AI models, while the terminal simply receives and implements the pre-calculated cycle parameters, reducing terminal complexity while still achieving energy savings through AI-driven optimization.
Data Source
AI summary
A method for determining a discontinuous reception (DRX) cycle, includes: receiving a first DRX cycle sent by a network device, in which the first DRX cycle is determined on the basis of an artificial intelligence (AI) model, and the AI model corresponds to a service type set which are operated by the terminal.