Neural Network CPE Estimation for 5G Phase Noise
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Solution Overview
Problem
Current common phase error (CPE) estimation methods in wireless communication, particularly in 5G systems, face limitations when only a front-loaded demodulation reference signal is available or when the time density of phase tracking reference signals is large, leading to inaccurate phase noise compensation and inter-carrier interference, which affects signal quality and block error rate performance.
Innovation Solution
A learning-based method using an artificial neural network to refine CPE estimates by receiving and modifying CPE values for symbols in a slot, interpolating values for non-reference symbols, and outputting improved estimates to a channel estimation module, trained using a measured phase noise model to enhance signal quality and robustness across varying channel conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional CPE estimation methods are used with limited reference signals, then device complexity is reduced, but measurement precision of phase noise compensation deteriorates
Solution Approach 1:
An artificial neural network is introduced as an intermediary component between the reference signal processing and channel estimation modules. The neural network receives CPE values from conventional estimators and transforms them into refined CPE estimates, thereby improving measurement precision without requiring fundamental changes to the overall system architecture.
Solution Approach 2:
The system changes the parameter representation of CPE values by transforming them through a neural network that learns optimal transformation parameters from training data. This parameter transformation enables more accurate phase noise compensation while maintaining compatibility with existing system components.
2Measurement precision
If more reference signals are used for CPE estimation, then measurement precision improves, but loss of time increases
Solution Approach 1:
The neural network applies partial action by refining only the essential CPE parameters rather than reprocessing all reference signal data. This selective refinement achieves improved measurement precision with reduced processing time compared to conventional methods that process complete reference signal sets.
Solution Approach 2:
The neural network is pre-trained offline with extensive phase noise characteristics data, performing preliminary learning of phase noise patterns. During actual operation, this pre-acquired knowledge enables rapid CPE estimation without requiring extensive real-time processing of reference signals.
3Measurement precision
If conventional interpolation is used for non-RS symbols, then device complexity is minimized, but measurement precision deteriorates when PTRS time density is large
Solution Approach 1:
The neural network serves as an intermediary that receives CPE values from both RS and non-RS symbols and performs intelligent interpolation for non-RS symbols. This neural intermediary learns optimal interpolation patterns from training data, achieving superior precision compared to conventional linear or polynomial interpolation methods.
Solution Approach 2:
The system changes the interpolation approach by transforming the interpolation problem into a neural network parameter optimization problem. The neural network learns parameter sets that optimize interpolation accuracy for different PTRS time density configurations, thereby improving measurement precision without increasing algorithmic complexity.
Data Source
AI summary
A method of modifying a common phase error (CPE) estimate of a slot including symbols, the method including receiving a CPE value corresponding to a symbol of a slot by an artificial neural network, generating a modified CPE value with the artificial neural network, and outputting the modified CPE value from the artificial neural network.