Impairment Correlation Estimation for Wireless Receiver Interference Suppression
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Solution Overview
Problem
Conventional RAKE receivers are ineffective against interference noise such as self-interference and multi-user access interference in wireless communication systems, as they rely on inaccurate estimation of impairment correlations for weighting factors.
Innovation Solution
A method and apparatus for deriving an impairment correlation matrix using first and second impairment correlation estimators to generate processing parameters, which are used to calculate weighting factors for coherent combination of despread symbols to suppress noise and interference, and to estimate signal-to-interference ratio (SIR) for rate adaptation and transmit power control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional RAKE receiver is used to combine multi-path signals, then signal-to-noise ratio is improved, but interference noise such as self-interference and multi-user access interference cannot be suppressed
Solution Approach 1:
The patent changes the parameter of weighting factors from conventional fixed values to adaptive values based on estimated impairment correlations. By calculating weighting factors as w = R^-1 * c where R is the impairment correlation matrix and c is the channel estimate vector, the system dynamically adjusts parameters to suppress interference while maintaining signal strength.
Solution Approach 2:
The patent implements feedback by continuously estimating impairment correlations from received signals and using these estimates to update weighting factors. The correlation processor estimates impairment correlations from despread symbols and feeds back to generate updated weighting factors for the combiner, creating a closed-loop system that adapts to changing interference conditions.
2Object-affected harmful factors
If generalized RAKE receiver with impairment correlation based weighting factors is used, then interference suppression is achieved, but accuracy of impairment correlation estimation is critical for success
Solution Approach 1:
The patent segments the impairment correlation estimation into multiple independent estimators (first impairment correlation estimator and second impairment correlation estimator). Each estimator processes different aspects of the signal independently, and their results are combined by the correlation processor. This segmentation allows each estimator to specialize in specific correlation aspects, improving overall estimation accuracy.
Solution Approach 2:
The patent creates a composite estimation system by combining multiple impairment correlation estimators with different processing approaches. The first estimator may use parametric methods while the second uses non-parametric methods, and the correlation processor integrates these complementary approaches to produce a comprehensive impairment correlation matrix that captures various interference characteristics.
3Measurement precision
If multiple impairment correlation estimators are used to derive impairment correlation matrix, then interference suppression accuracy is improved, but device complexity increases
Solution Approach 1:
The patent designs the impairment correlation estimators to serve multiple functions: they estimate impairment correlations for weighting factor generation, provide inputs for correlation processing, and enable both interference suppression and signal quality assessment. The correlation processor universally handles both the estimation of impairment correlations and the generation of processing parameters, reducing redundant components.
Data Source
AI summary
A method and apparatus derives an impairment correlation matrix to process signals received at a wireless receiver over multiple paths of a multi-path channel. The receiver includes first and second impairment correlation estimators for estimating first and second impairment correlation matrices based on despread symbols received over multiple paths of a multi-path channel. The receiver then derives the impairment correlation matrix based on the estimated first and second impairment correlation matrices. The receiver may combine traffic despread values to suppress interference using weighting factors calculated based on the derived impairment correlation matrix. Further, the receiver may estimate a signal-to-interference ratio based on the derived impairment correlation matrix.