Phase Noise Compensation Using Low-Frequency Spectral Estimation
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Solution Overview
Problem
Existing methods for phase noise mitigation in single carrier wireless communication systems are inadequate, particularly when pilot signals are separated by significant time intervals, leading to inaccurate phase noise estimation and high computational complexity, which limits their practical application.
Innovation Solution
A method that estimates phase noise realization from a sequence of signal samples using a priori known statistical characteristics, calculating low-frequency spectral components via a linear combination with weighted coefficients, and applying these estimates for phase noise compensation across multiple signal samples, reducing computational complexity and improving estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional phase noise estimation methods using pilot symbols are applied, then phase noise can be estimated, but the estimation accuracy deteriorates when pilot symbols are separated by significant time intervals
Solution Approach 1:
The patent performs preliminary phase noise estimation using available pilot symbols, then uses this preliminary estimate as a foundation for subsequent refinement. The preliminary estimate captures the general phase noise trend, which is then improved by incorporating correlations between phase noise samples to fill in gaps between widely separated pilots.
Solution Approach 2:
The patent employs an iterative feedback mechanism where initial phase noise estimates are refined by exploiting temporal correlations. The estimated phase noise is fed back into the estimation process to improve subsequent estimates, allowing the system to progressively reduce estimation errors even when pilots are far apart in time.
2Measurement precision
If phase noise estimation is performed for each symbol using decision-directed approach, then continuous phase noise tracking is achieved, but computational complexity increases significantly
Solution Approach 1:
Instead of performing full decision-directed phase noise estimation for every single symbol, the patent applies a simplified estimation approach that focuses on key moments (pilot symbols) and then interpolates or correlates to derive phase noise for intermediate symbols. This partial action reduces computational burden while maintaining sufficient tracking accuracy.
Solution Approach 2:
The patent introduces phase noise correlation as an intermediary mechanism that bridges the gap between discrete pilot symbols. By modeling the temporal correlation of phase noise, the system can infer phase noise values for symbols between pilots without performing direct estimation for each symbol, thus reducing complexity while maintaining continuity.
3Device complexity
If linear interpolation is used to approximate phase noise between pilot signals, then computational complexity is reduced, but estimation accuracy deteriorates when phase noise deviates from linear trend
Solution Approach 1:
The patent changes the fundamental parameter model from simple linear interpolation to a correlation-based model that captures the statistical properties of phase noise. By modeling phase noise as a correlated stochastic process rather than a linear function, the system can accurately track non-linear phase noise variations while maintaining computational efficiency through closed-form correlation expressions.
Data Source
AI summary
The present invention generally relates to the field of electrical communication and more specifically to apparatuses and methods of phase noise mitigation for signal transmission in wideband telecommunication systems.The method for compensation of the phase noise effect on the data transmission through a radio channel is based on a possibility to present the phase noise of a reference oscillator like a random process where the main spectral density is concentrated in the low-frequency region. Therefore, the number of estimated parameters can be reduced many times to several low-frequency spectral components instead of a direct estimation in the time domain.The advantage of the method is an improvement of the estimation accuracy and a reduction of the computational complexity.