DRX Parameter Configuration Using Historical Transmission Data

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Solution Overview

Problem

Current methods for configuring discontinuous reception (DRX) parameters in wireless communication systems face challenges such as high power consumption due to delayed and inefficient data interactions between terminals and base stations, limited computing capability in terminals, and security risks associated with data privacy, which affect battery life and user experience.

Innovation Solution

A method and apparatus that configure DRX parameters using historical DRX transmission feature data, involving a neural network model trained on past data to predict service types and select optimal DRX parameters based on power consumption thresholds, reducing the need for real-time auxiliary information from terminals and minimizing signaling overhead.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If DRX parameters are configured using real-time auxiliary information from terminals, then DRX parameter optimization is improved, but power consumption increases and data privacy security risks worsen

Engineering Contradiction:
ImproveDRX parameter optimizationVSAvoidpower consumption
Core Design Contradiction:
Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The base station performs preliminary actions by acquiring historical DRX transmission feature data from terminal equipment before configuring DRX parameters. The neural network model is trained in advance using this historical data, enabling the base station to predict service types and determine optimal DRX parameters without requiring real-time auxiliary information transmission from the terminal, thus reducing power consumption while maintaining optimization effectiveness

Inventive Principle:
Principle #10Preliminary action

2Adaptability or versatility

If DRX parameters are configured using real-time auxiliary information from terminals, then DRX parameter optimization is improved, but data privacy security risks worsen

Engineering Contradiction:
ImproveDRX parameter optimizationVSAvoiddata privacy security risks
Core Design Contradiction:
Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The invention extracts and utilizes only the necessary historical DRX transmission feature data from the terminal equipment to train the neural network model. By taking out only the essential features (such as traffic patterns, transmission timing) rather than requiring comprehensive real-time auxiliary information, the system achieves DRX parameter optimization while minimizing data privacy security risks associated with transmitting and processing sensitive terminal data

Inventive Principle:
Principle #2Taking out (Extraction)

3Adaptability or versatility

If terminal equipment transmits auxiliary information to base station for DRX configuration, then DRX parameter optimization is improved, but signaling overhead increases

Engineering Contradiction:
ImproveDRX parameter optimizationVSAvoidsignaling overhead
Core Design Contradiction:
Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The invention extracts only the essential historical DRX transmission feature data needed for neural network model training, eliminating the need for terminals to transmit comprehensive auxiliary information. This selective extraction approach reduces signaling overhead while still providing sufficient data for the base station to accurately predict service types and optimize DRX parameters

Inventive Principle:
Principle #2Taking out (Extraction)

4Use of energy by moving object

If DRX parameters are not optimized, then power consumption is reduced, but battery life decreases

Engineering Contradiction:
Improvepower consumptionVSAvoidbattery life
Core Design Contradiction:
Use of energy by moving objectVSDuration of action of moving object

Solution Approach 1:

The invention changes DRX parameters dynamically by predicting service types using a neural network model trained on historical transmission features. The base station adjusts DRX parameters (such as active time, sleep time, reactivation timing) based on predicted service characteristics, enabling the system to optimize the balance between power consumption and battery life by adapting parameters to actual transmission needs rather than using fixed or overly aggressive power saving settings

Inventive Principle:
Principle #35Parameter changes

Data Source

PatentUS20230422343A1Method and apparatus for configuring discontinuous reception parameter, and communication device and storage medium
Publication Date: 2023.12.28 BEIJING XIAOMI MOBILE SOFTWARE CO LTD
  • US20230422343A1 patent drawing
  • US20230422343A1 patent drawing
  • US20230422343A1 patent drawing

AI summary

Aspects of the invention can provide a method for configuring a discontinuous reception (DRX) parameter, which method is applied to a base station. The method can include, according to first discontinuous reception (DRX) transmission feature data, configuring a discontinuous reception (DRX) parameter for a terminal, wherein the first discontinuous reception (DRX) transmission feature data is discontinuous reception (DRX) transmission feature data for discontinuous reception (DRX) transmission performed by the terminal at historical moments.