Deep Learning CSI Encoder for Massive MIMO Feedback
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Solution Overview
Problem
Existing Massive MIMO systems face challenges in efficiently compressing and restoring channel state information (CSI) due to non-sparse channel characteristics, inadequate utilization of channel characteristics, and high computational complexity in existing compressive sensing methods.
Innovation Solution
A communication system and codec method utilizing deep learning to encode and decode CSI between electronic apparatuses, leveraging correlations between channel state information to improve CSI decoding performance, with a CSI encoder and decoder employing deep learning functions to encode and compress CSI into codewords for feedback-based restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If compressive sensing is used to compress CSI, then spectrum efficiency is improved, but computational complexity increases and restoration timeliness deteriorates
Solution Approach 1:
The patent replaces traditional compressive sensing algorithms (which rely on iterative mathematical optimization) with a deep learning-based system that uses neural networks to directly map compressed measurements to channel state information. This substitution of the computational approach reduces complexity while maintaining compression benefits
Solution Approach 2:
The patent changes the fundamental parameters of the compression-restoration process by training deep neural networks on channel data to learn optimal compression and restoration transformations. This allows the system to achieve high compression ratios with reduced computational complexity compared to traditional CS methods
2Quantity of substance
If compressive sensing algorithms are used to restore CSI, then compression is achieved, but restoration timeliness deteriorates due to multiple iterations
Solution Approach 1:
The patent replaces iterative mathematical restoration algorithms with direct neural network inference that provides instant restoration results. The trained neural network can restore CSI from compressed measurements in a single forward pass, eliminating the time-consuming iterations required by traditional CS algorithms
Solution Approach 2:
The patent performs preliminary training of the neural network model using extensive channel data before actual operation. During normal operation, the pre-trained network can instantly restore CSI without requiring iterative computation, as all complex transformations were learned in advance during training
3Quantity of substance
If random projection is used in CS compression, then data compression is achieved, but channel characteristics are not fully utilized
Solution Approach 1:
The patent applies local quality by training different regions of the neural network to specialize in processing specific aspects of channel characteristics. The network architecture includes dedicated layers and parameters that are tuned to capture and preserve important channel features while discarding redundant information, thereby fully utilizing channel characteristics during compression
Solution Approach 2:
The patent changes the compression approach from fixed random projection to adaptive neural network transformations that are trained on actual channel data. This allows the system to learn and preserve the specific statistical properties and correlations of different channel types, fully utilizing channel characteristics rather than applying generic compression
Data Source
AI summary
A communication system and a codec method based on deep learning and known channel state information (CSI) are provided. The communication system includes: a first electronic apparatus including a known first link CSI and a CSI encoder having a deep learning function; and a second electronic apparatus including a known second link CSI and a CSI decoder having a deep learning function. The first and second link CSIs have a correlation or a similarity. The CSI encoder of the first electronic apparatus encodes or compresses the first link CSI into the first codeword, and feeds the first codeword back to the second electronic apparatus via a feedback link. The CSI decoder of the second electronic apparatus encodes or compresses the second link CSI into a second codeword, and decodes or restores the first link CSI of the first electronic apparatus based on the first codeword and the second codeword.