Network Node Channel Estimation for High-Speed Train Frequency Offset
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Solution Overview
Problem
Existing channel estimation methods for high-speed train communication scenarios fail to accurately estimate the frequency offset, leading to reduced cell capacity, high user access failure rates, and poor user experience.
Innovation Solution
A method performed by a network node that involves obtaining a first channel estimation value through frequency-domain channel estimation based on DMRS symbol decorrelation signals, determining the channel type using a neural network, and adjusting the frequency offset for high-speed train channels to obtain a second channel estimation value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing channel estimation methods are used for high-speed train communication, then the system can operate with standard methods, but the frequency offset estimation accuracy deteriorates leading to reduced cell capacity and high user access failure rates
Solution Approach 1:
The patent applies preliminary action by performing time offset estimation and compensation before frequency offset estimation. The method first estimates time offset based on DMRS symbol decorrelation signal, then uses this compensated signal for frequency offset estimation. This preliminary time synchronization prepares the signal in advance, enabling accurate frequency offset estimation that was previously unachievable with standard methods.
Solution Approach 2:
The patent replaces traditional mechanical frequency offset estimation methods with a neural network-based system. The neural network is trained offline to recognize frequency offset patterns and performs rapid, accurate frequency offset estimation online. This substitution of mechanical estimation with intelligent algorithms achieves superior measurement precision for high-speed train scenarios.
2Reliability
If existing channel estimation methods are used, then the system complexity remains low, but user access failure rate increases and user experience deteriorates
Solution Approach 1:
The patent segments the channel estimation process into distinct modules: time offset estimation module, time offset compensation module, frequency offset estimation module (using neural network), and channel estimation module. This segmentation allows each module to be optimized independently while working together to improve overall reliability. The modular approach manages complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The neural network is trained offline in advance with大量 training data to learn frequency offset patterns. This preliminary training action prepares the model for rapid, accurate inference during actual operation, improving user access success rate without adding real-time computational complexity to the main processing flow.
3Measurement precision
If frequency offset is not accurately estimated, then the system operates with standard procedures, but channel estimation accuracy deteriorates leading to poor user experience
Solution Approach 1:
The system applies self-service by automatically performing time offset compensation and frequency offset estimation without manual intervention. The neural network autonomously identifies frequency offset patterns from the decorrelation signal, and the system automatically adjusts frequency offset based on neural network predictions. This self-service capability maintains high channel estimation accuracy while preserving ease of operation.
Solution Approach 2:
The patent replaces manual or simple frequency offset estimation procedures with an intelligent neural network system that automatically performs complex pattern recognition and estimation, improving measurement precision while maintaining operational simplicity through automation.
Data Source
AI summary
A method performed by a network node is provided. The method includes obtaining a first channel estimation value of a channel between the network node and a user equipment, by performing frequency-domain channel estimation based on a decorrelation signal related to reference signal symbols received from the user equipment, determining whether a channel type of the channel is a high-speed train channel, and in accordance with a determination that the channel type of the channel is the high-speed train channel adjusting frequency offset of the channel, and obtaining a second channel estimation value based on the adjusted frequency offset and the first channel estimation value.