Adaptive Data Compression Manager for 5G Network Traffic
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Solution Overview
Problem
Current 5G system specifications do not efficiently manage large data transfers between network functions, leading to significant network traffic burdens and energy consumption, particularly in machine learning applications that require frequent data updates.
Innovation Solution
The introduction of a Data Compression Manager (DCM) that intelligently selects data compression information based on network conditions, data type, and consumer error tolerance, allowing for efficient data transfer while maintaining prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is transferred without compression in 5G network functions, then prediction accuracy is maintained, but network traffic volume increases significantly
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting compression parameters based on data characteristics and requirements. The system modifies compression ratios, algorithms, and settings to achieve optimal balance between data fidelity and traffic reduction, transforming fixed-parameter compression into adaptive parameter-based compression.
Solution Approach 2:
The patent implements dynamics by making the compression process adaptive and dynamic rather than static. The compression manager continuously adjusts compression strategies based on real-time data characteristics, network conditions, and consumer requirements, enabling the system to respond dynamically to changing conditions while maintaining prediction accuracy.
2Quantity of substance
If data compression is applied to reduce network traffic, then network traffic volume decreases, but prediction accuracy may be compromised
Solution Approach 1:
The patent employs feedback mechanisms where the compression manager receives information about prediction accuracy, data characteristics, and compression performance. This feedback loop enables continuous optimization of compression parameters to maintain prediction accuracy while maximizing traffic reduction, allowing the system to learn and adapt from operational results.
Solution Approach 2:
The system changes compression parameters dynamically based on feedback and data characteristics, adjusting compression ratios and algorithms to preserve critical information while reducing traffic. This parameter adaptation ensures that compression does not compromise prediction accuracy for machine learning applications.
3Device complexity
If traditional data transfer methods are used between network functions, then system complexity remains low, but energy consumption increases due to larger data transfers
Solution Approach 1:
The patent introduces a compression manager as an intermediary component between data producers and consumers. This mediator handles the complexity of compression and decompression operations, allowing simple data producers and consumers to benefit from efficient compression without directly implementing complex compression logic, thus managing system complexity centrally while reducing energy consumption.
4Ease of manufacture
If no data compression is used, then implementation simplicity is maintained, but network bandwidth utilization becomes inefficient
Solution Approach 1:
The patent creates a universal compression manager that serves multiple network functions and data types through a single standardized interface. This multi-functional component handles compression for various machine learning applications and data formats, providing efficient bandwidth utilization while maintaining implementation simplicity through a unified approach rather than multiple specialized solutions.
Data Source
AI summary
A Data Compression Manager (DCM) that can help a data provider (DP) and a data consumer (DC) to establish a data transfer using an advantageous data compression method. In one embodiment, when a DC wants to get some data from a DC, the DP will contact the DCM and the DCM will then choose data compression information based on (i.e., based at least on), for example, current conditions within the network (e.g., a maximum available network bandwidth), information about the data to be compressed (e.g., the type of the data), and/or the DC's data error tolerance. Then the DCM will indicate to the DP the chosen data compression information. The DP will then i) inform the DC of the compression information that is needed by the DC to decompress the data and ii) provide the requested data to the DC in compressed form.