AI Channel Image Generation for Wireless Communication Systems
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Solution Overview
Problem
Existing methods for acquiring channel images in wireless communication systems are limited by the need for expensive hardware resources, high implementation costs, and constraints due to device size and power consumption, particularly in systems with fewer antenna units, leading to poor channel estimation performance and limited universality of artificial intelligence applications.
Innovation Solution
A novel method involving off-line training and online inference using a data-driven artificial intelligence neural network, which includes dimension reconstruction, sliding window selection, and separation of real and imaginary parts to generate channel images, enabling effective channel estimation with reduced hardware requirements and improved universality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional channel image acquisition methods using lens antenna arrays are adopted, then channel images can be obtained with geometric characteristics, but hardware resources requirements increase significantly and implementation cost rises
Solution Approach 1:
The patent creates virtual channel images through signal processing algorithms that simulate the geometric characteristics of lens antenna arrays without requiring physical lens structures. By copying the functional effect of lens antennas through computational methods, the system achieves channel image acquisition with reduced hardware complexity while maintaining estimation performance
Solution Approach 2:
The patent replaces the mechanical/optical lens antenna array structure with signal processing operations. Instead of using physical lens elements to focus and shape antenna patterns, the system uses digital signal processing to achieve the same channel image formation, substituting mechanical complexity with computational simplicity
2Reliability
If data-driven channel estimation with large antenna arrays is used, then sufficient channel data for training can be obtained, but device size and power consumption constraints are violated
Solution Approach 1:
The patent uses partial channel information and sliding window techniques to process only the necessary portion of channel data at each step. By processing channel images in smaller windows rather than requiring complete large-scale array data, the system achieves reliable estimation with reduced computational power consumption suitable for mobile devices
Solution Approach 2:
The patent segments the channel estimation process into manageable components using sliding windows that process local regions of the channel matrix. This segmentation allows the system to achieve accurate channel estimation without requiring the device to handle and process complete large-scale antenna array data simultaneously, reducing power consumption and computational burden
3Ease of operation
If least square channel estimation is applied, then implementation simplicity is maintained, but noise amplification occurs in poor channel conditions
Solution Approach 1:
The patent introduces channel images as an intermediary representation that captures the geometric characteristics of wireless channels. By first transforming channel data into image format that emphasizes spatial structures, the system can then apply estimation algorithms that are both simple to implement and robust to noise, avoiding the direct noise amplification issues of traditional least square methods
Data Source
AI summary
Disclosed herein is a method and a system for acquiring channel images, relating to the field of cross-integration of artificial intelligence neural network and wireless communication system. Based on data-driven artificial intelligence neural network channel estimation, the disclosure obtains a sufficient number of channel images for training the neural network. It overcomes the limitation that the traditional acquisition of channel images dependent on the types of deployed antennas and geometric dimensions, so that the artificial intelligence neural network can be effectively used for channel estimation of wireless communication systems in practice.