Transform-Base Data Compression for Wireless Transmission Overhead
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Solution Overview
Problem
In next-generation wireless communication systems, the direct processing and transmission of data oriented to new scenarios, such as imaging and AI data, leads to excessive occupation of channel resources, necessitating a reduction in transmission overheads.
Innovation Solution
A unified compression framework based on transform bases is employed to compress data flexibly according to its type, using preconfigured or real-time determined transform base indication information to enhance compression efficiency and reduce data transmission overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data oriented to new scenarios is directly processed and transmitted, then transmission reliability is ensured, but channel resource occupation increases excessively
Solution Approach 1:
The patent extracts and transmits only the essential features of data oriented to new scenarios through feature extraction networks, rather than transmitting the complete original data. This selective extraction reduces channel resource occupation while maintaining transmission reliability by preserving the most important data characteristics.
Solution Approach 2:
The patent transforms data from its original form into a different parameter representation through feature extraction and transformation networks. By changing the data parameters and representation form, the system achieves more efficient channel resource utilization while preserving the essential information needed for reliable transmission.
2Productivity
If data is compressed using a unified framework, then compression efficiency is improved, but adaptability to different data types decreases
Solution Approach 1:
The patent employs dynamic and configurable transformation networks that can be adapted to different data types. The feature extraction and transformation mechanisms are designed to be flexible, allowing the system to adjust its compression approach based on the specific characteristics of the input data, thereby maintaining both compression efficiency and data type adaptability.
Solution Approach 2:
The patent creates a universal compression framework that can handle multiple data types through multi-functional feature extraction networks. The same basic framework structure is used across different data types, but with configurable parameters and transformation functions that adapt to specific data characteristics, achieving both universality and adaptability.
3Loss of substance
If transform base indication information is preconfigured, then configuration overhead is reduced, but compression performance flexibility is limited
Solution Approach 1:
The patent performs preliminary configuration of transform base indication information, preparing compression parameters in advance. This preliminary action reduces the need for complex real-time configuration overhead during actual compression operations, while the configurable nature of the pre-set parameters allows for adequate compression performance across different scenarios.
Data Source
AI summary
A communication method and apparatus are provided. The method includes: obtaining to-be-compressed data, where the to-be-compressed data includes first data; determining at least one first transform base based on a first data type of the first data; determining second data based on the at least one first transform base and the first data; and sending the second data to a first device. According to the method provided in this application, a unified compression framework based on a transform base is proposed for the to-be-compressed data. At least one first transform base can be flexibly determined based on a data type of the first data, so that the first data is compressed based on the at least one first transform base. This ensures compression performance, enhances compression efficiency, and reduces data transmission overheads.