CSI Feedback Error Correction via Machine Learning
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately reconstructing channel state information (CSI) due to imperfect downlink and uplink channel estimations, which affects the accuracy of CSI feedback and leads to suboptimal precoder selection.
Innovation Solution
The proposed solution involves an apparatus and method that receive and decode data from a device in a wireless communication system, generating a second estimate of CSI by modifying the first estimate based on error indications and error correction, utilizing machine learning modules for error detection and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If channel state information is transmitted from device to network node over wireless channel, then CSI feedback is provided for precoder selection, but errors are introduced during transmission affecting reconstruction accuracy
Solution Approach 1:
The patent implements a feedback mechanism where the network node detects errors in received CSI data using a machine learning-based error detection module, generates error indications, and feeds them back to the transmitting device. The device then uses these error indications to correct the CSI data before retransmission, creating a closed-loop system that progressively improves CSI reconstruction accuracy while maintaining reliable feedback.
Solution Approach 2:
The patent introduces machine learning modules as intermediaries between the transmitting device and network node. These ML modules serve as intelligent mediators that detect errors, generate error indications, and correct CSI data, bridging the gap between imperfect wireless transmission and the requirement for accurate CSI reconstruction without requiring complex traditional error correction codes.
2Reliability
If traditional error correction codes are used for CSI data transmission, then transmission reliability improves, but communication overhead increases
Solution Approach 1:
The patent employs lightweight machine learning-based error detection and correction modules that are computationally efficient and require minimal overhead compared to traditional error correction codes. These ML modules process CSI data with low complexity, providing reliable error detection and correction without the substantial overhead associated with conventional coding schemes, making them suitable for resource-constrained wireless communication systems.
Solution Approach 2:
The patent changes the operational parameters of error detection and correction by using machine learning models with adjustable complexity and confidence thresholds. The system can adapt the level of error correction applied based on channel conditions and required accuracy, dynamically adjusting parameters to balance reliability and overhead rather than using fixed, conservative error correction codes.
3Measurement precision
If machine learning modules are used for error detection and correction, then CSI reconstruction accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the error detection and correction functionality into separate machine learning modules: an error detection module that identifies errors in received CSI data, an error indication generation module that creates correction signals, and an error correction module that applies corrections. This segmentation allows each module to be optimized independently and enables modular deployment where only necessary components are implemented based on system requirements.
Solution Approach 2:
The patent implements dynamic machine learning modules that can adapt their operation based on input characteristics and system conditions. The error detection and correction ML modules can adjust their processing depth, confidence thresholds, and correction aggressiveness dynamically, allowing the system to maintain high accuracy while reducing complexity when error conditions are minimal or well-understood.
Data Source
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AI summary
A method, apparatus and computer program is described comprising: receiving data transmitted over a wireless channel (the data encoding a first estimate of a channel state information of the wireless channel); decoding said data to generate a second estimate of the channel state information of the wireless channel; and generating a channel state information output by modifying the second estimate of the channel state information based, at least in part, on a first error indication.