Wireless Communication Model Quantization for Low-Overhead CSI Feedback
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Solution Overview
Problem
Existing communication systems face challenges in supporting multiple connection types and scenarios, such as D2D, M2M, V2V, and V2X, while efficiently managing AI model sizes and CSI feedback overheads, particularly in resource-constrained terminal devices.
Innovation Solution
Implementing model quantization techniques, including quantization aware training (QAT) and post-training quantization (PTQ), to reduce AI model sizes and improve inference efficiency, combined with AI-based beam management and CSI feedback using autoencoders to optimize channel information feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI model precision is maintained at high levels, then model accuracy is improved, but model size and memory consumption increase
Solution Approach 1:
The patent applies parameter changes by transforming model parameters from high-precision floating-point format to low-precision quantized format. This involves changing the numerical representation parameters (bit depth, data type) to reduce model size while maintaining acceptable accuracy through quantization techniques that preserve essential information.
Solution Approach 2:
The patent extracts and removes redundant or less important information from the model parameters during quantization. By identifying and eliminating unnecessary precision in certain model components, the model size is reduced while retaining the core functionality and accuracy required for effective operation.
2Quantity of substance
If model size is reduced through quantization, then memory consumption is lowered, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by performing quantization aware training (QAT) during the model training phase. This preliminary quantization preparation embeds quantization parameters and scaling factors into the model structure in advance, so that during inference, the computational operations are simplified rather than complicated, reducing actual computational complexity during deployment.
3Measurement precision
If quantization precision is increased, then model accuracy is maintained, but power consumption increases
Solution Approach 1:
The patent applies local quality by using different quantization precisions for different parts of the model based on their importance and sensitivity. Critical layers or parameters that require higher accuracy maintain higher precision, while less sensitive components use lower precision, optimizing the balance between accuracy and power consumption by avoiding uniform high-precision quantization across the entire model.
Data Source
AI summary
The present application relates to a communication method and device. The method includes: a first communication device receives model quantization configuration information from a second communication device; and the first communication device executes a model quantization related operation according to the model quantization configuration information.