Selective Dictionary Updates for Wireless Data Compression
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Solution Overview
Problem
Existing data compression technologies in wireless communication face challenges in achieving high accuracy while minimizing resource consumption, particularly in scenarios involving large data amounts and AI/ML applications, where current methods fail to efficiently update dictionaries for improved compression performance.
Innovation Solution
A communication method utilizing a dynamic dictionary that allows for selective updating of dictionary subsets based on training data, reducing computing and transmission resources by sending only subset-specific update information, thereby enhancing compression accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional data compression technologies (DCT, DFT, DWT, entropy coding) are used with high compression rates, then data transmission resource consumption is reduced, but data transmission performance deteriorates
Solution Approach 1:
The patent applies dynamics by making the dictionary adaptive and updateable. The dictionary is initially constructed from training data and then dynamically updated based on feedback from compression performance evaluation. This allows the compression system to adapt to different data characteristics, resolving the contradiction between compression rate and transmission performance by optimizing the dictionary for specific data patterns.
Solution Approach 2:
The patent changes parameters by adjusting dictionary subsets based on compression performance. Different subsets of the dictionary are selected and updated depending on the evaluation results of compression operations. This parameter adjustment allows the system to optimize compression rates while maintaining transmission performance, directly addressing the technical contradiction.
2Measurement precision
If traditional data compression technologies are used with low compression rates, then data transmission performance is maintained, but transmission resource overheads increase
Solution Approach 1:
The patent optimizes compression parameters by evaluating different dictionary subsets and selecting those that achieve the best compression performance. This parameter optimization allows the system to maintain high transmission performance while achieving higher compression rates, thereby reducing transmission resource overheads.
Solution Approach 2:
The patent implements feedback mechanisms where compression performance is evaluated and used to update the dictionary. This feedback loop enables the system to learn from compression results and continuously improve compression efficiency, maintaining transmission performance while reducing resource overheads through optimized compression parameters.
3Measurement precision
If complete dictionary update information is transmitted, then compression accuracy is improved, but wireless transmission resource consumption increases
Solution Approach 1:
The patent extracts only the necessary subset update information from the complete dictionary update. Instead of transmitting entire dictionary updates, it identifies and transmits only the specific subset information that needs updating based on compression performance evaluation. This extraction principle reduces transmission resource consumption while maintaining compression accuracy.
Solution Approach 2:
The patent segments the dictionary into multiple subsets and updates only the relevant segments rather than the entire dictionary. This segmentation allows selective transmission of update information for specific subsets, reducing wireless transmission resource consumption while ensuring compression accuracy is maintained through targeted updates.
4Measurement precision
If dictionary size is increased to improve compression performance, then compression accuracy improves, but computing resource consumption increases
Solution Approach 1:
The patent segments the large dictionary into multiple smaller subsets, allowing selective updating and processing. This segmentation reduces the computing burden by enabling the system to work with smaller, manageable subsets rather than processing the entire large dictionary, thereby reducing computing resource consumption while maintaining compression accuracy through selective subset updates.
Solution Approach 2:
The patent applies partial action by updating only the necessary dictionary subsets rather than the complete dictionary. Based on compression performance evaluation, only specific subsets are updated, which reduces computing resource consumption compared to updating the entire dictionary, while still maintaining compression accuracy through targeted improvements in critical subsets.
Data Source
AI summary
A method includes obtaining, by a first communication apparatus, training data used to train a dictionary used for data compression. The method also includes determining a group of to-be-updated subsets in the dictionary based on the training data. The method further includes determining dictionary update information corresponding to the group of to-be-updated subsets. The method additionally includes sending the dictionary update information.