Codebook-Based Data Quantization for Lower Transmission Overhead
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Solution Overview
Problem
The increasing demand for large-scale data transmission between devices leads to significant resource overheads, necessitating a solution to optimize transmission efficiency.
Innovation Solution
A communication method involving the use of codebooks for quantizing data, where codebooks are determined based on data distribution features to reduce transmission resource overheads, with joint determination between apparatuses ensuring quantization performance and reduced overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large-scale data is transmitted between devices, then data transmission capability is improved, but transmission resource overheads increase
Solution Approach 1:
The patent extracts only the essential features of the data (distribution characteristics) and uses a codebook to represent the data in a compressed form. Instead of transmitting the entire large-scale dataset, only the codebook index and quantized residuals are transmitted, significantly reducing transmission resource overheads while maintaining data transmission capability.
Solution Approach 2:
The patent changes the representation parameters of the data by using codebooks with different distribution characteristics (e.g., Gaussian, uniform, exponential distributions). By selecting and switching between different codebooks based on data characteristics, the system optimizes the balance between transmission efficiency and reconstruction accuracy, reducing resource overheads while maintaining productivity.
2Measurement precision
If codebook size is increased to improve quantization performance, then quantization accuracy is improved, but transmission overhead for indicating codebook increases
Solution Approach 1:
The patent segments the codebook selection process by dividing codebooks into multiple groups based on data distribution characteristics. Instead of selecting from a single large codebook set, the system first selects a group based on distribution type, then selects a specific codebook within that group. This hierarchical segmentation reduces the indication overhead while maintaining quantization accuracy.
Solution Approach 2:
The patent performs preliminary classification of data distribution characteristics before codebook selection. By pre-categorizing data into different distribution types (Gaussian, uniform, exponential, etc.), the system narrows down the codebook search space in advance, reducing the indication overhead required to specify the final codebook while maintaining quantization performance.
3Quantity of substance
If quantization is applied to reduce transmission overhead, then transmission resource overheads are reduced, but quantization error increases
Solution Approach 1:
The patent implements a feedback mechanism where the receiver calculates the actual quantization error based on the received codebook index and quantized data. This feedback information is used to adjust and optimize the codebook selection and quantization parameters in subsequent transmissions, progressively reducing quantization error while maintaining reduced transmission overheads.
Solution Approach 2:
The patent makes the quantization process dynamic by allowing the system to switch between different codebooks and adjust quantization parameters based on the actual data characteristics and channel conditions. This dynamic adaptation enables the system to minimize quantization error for each specific transmission scenario while maintaining overall transmission efficiency.
Data Source
AI summary
A method includes: determining a first codebook, where the first codebook is for quantizing first data; and sending first indication information, where the first indication information indicates the first codebook. In this solution, the first data is quantized by using the codebook, so that transmission resource overheads during data transmission can be reduced.