RL-Based Data Transmission Control for Changing Network Conditions
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Solution Overview
Problem
Existing congestion control algorithms are inflexible and struggle to adapt to changing network conditions, leading to degraded data transmission performance and unstable network performance in new scenarios.
Innovation Solution
A data transmission control method utilizing reinforcement learning (RL) models to analyze historical network performance data, group data transmission objects, and calculate tailored data transmission parameters for each object group, enabling adaptive control based on real-time network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning model is used to enable adaptive control, then adaptability to changing network conditions is improved, but device complexity increases
Solution Approach 1:
The patent segments data transmission objects into multiple object groups based on network conditions and transmission characteristics. Each group is controlled independently with group-specific parameters, allowing adaptive response to different network scenarios while managing complexity through modular group-based control rather than individual object control
Solution Approach 2:
The patent dynamically adjusts data transmission parameters (such as transmission rate, buffer size, etc.) based on real-time network conditions and object group characteristics. The reinforcement learning model learns optimal parameter configurations for different scenarios, enabling adaptability without hardcoding complex decision logic
2Productivity
If fine-grained parameter settings for each object group are implemented, then data transmission efficiency is improved, but calculation complexity increases
Solution Approach 1:
The patent merges objects with similar transmission characteristics into object groups, applying the same parameters and control strategies to multiple objects simultaneously. This reduces calculation complexity by processing groups rather than individual objects, while still maintaining fine-grained control at the group level to preserve transmission efficiency
3Measurement precision
If reinforcement learning model processes comprehensive network performance data, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent extracts and processes only the most critical network performance indicators relevant to each object group's transmission characteristics. Rather than processing all available network data, the system identifies and focuses on key metrics (such as bandwidth, latency, packet loss) that have the greatest impact on transmission optimization, reducing processing time while maintaining measurement precision
Data Source
AI summary
A method includes obtaining historical network performance data of each of data transmission objects, calculating, based on the historical network performance data, group network performance data of each of object groups, performing weighted calculation on the group network performance data of the object groups to obtain comprehensive network performance data of the data transmission objects, inputting the comprehensive network performance data and the group network performance data into a trained RL model to obtain a data transmission quality index of each object group, for each object group, determining a data transmission parameter of the object group based on the data transmission quality index and the group network performance data of the object group, and determining, in response to receiving a data transmission request transmitted by a target data transmission object, a target data transmission parameter based on a target object group to which the target data transmission object belongs.