MISO Predistortion Feedback Using Uncorrelated Noise Separation
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Solution Overview
Problem
MISO systems face challenges with multicollinearity, leading to poor identification performance and inability to correctly solve for parametric coefficients, which affects the reliability of individual regressor contributions and overall system efficiency.
Innovation Solution
The introduction of uncorrelated noise patterns in each input branch of the MISO system, mixed with input signals and processed through pre-distorters and signal paths, allows for the separation of contributions and determination of pre-distortions to optimize RF performance and efficiency, using a combiner and feedback mechanism to adjust paths and improve linearization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MISO systems are used without noise patterns, then the hardware architecture is simpler, but the identification performance deteriorates and parametric coefficients cannot be correctly solved
Solution Approach 1:
Uncorrelated noise patterns are introduced as intermediary signals in each input branch to enable the separation of contributions from different input signals. These noise patterns act as mediators that allow the feedback signal to be decomposed into individual path contributions, solving the multicollinearity problem without requiring complex hardware modifications.
Solution Approach 2:
The system changes the parameters of the input signals by adding uncorrelated noise patterns to each input branch. This parameter modification transforms the identification problem, making it possible to correctly solve for parametric coefficients and improve identification performance while maintaining the existing hardware architecture.
2Reliability
If RF switches are used for feedback in MISO systems, then individual path feedback can be obtained, but the hardware complexity increases and power loss occurs
Solution Approach 1:
The invention extracts the individual path contributions from the combined feedback signal by using uncorrelated noise patterns as identifiers. Instead of using RF switches to physically separate feedback paths, the system extracts the contribution of each input signal mathematically, eliminating the need for complex switching hardware while maintaining feedback accuracy.
Solution Approach 2:
The mechanical RF switching system is replaced with a signal processing approach. Uncorrelated noise patterns are used to encode individual path information, and the contribution separator uses signal processing to decode and separate the contributions, substituting mechanical switching with electronic signal manipulation.
3Reliability
If multicollinearity is present in MISO systems, then the system structure is simpler, but the parametric coefficients cannot be correctly solved and reliability deteriorates
Solution Approach 1:
Uncorrelated noise patterns serve as intermediary identifiers that break the multicollinearity between input signals. By adding these unique noise patterns to each input branch, the system creates distinguishable signal characteristics that allow reliable separation and identification of individual path contributions, enabling correct solution of parametric coefficients.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies hardware architecture, enhances RF performance by eliminating RF switches and optimizing power combining, enabling real-time adaptation to changes and improved system efficiency, even in non-isolated power combiners.
Implementation Method 1
The second number of mixers, each corresponding to a respective different one of the noise generator output ports and adapted to mix the respective input signal with the respective noise to generate a respective mixed signal
Data Source
AI summary
It is provided a method for providing feedback to pre-distorters in branches of a MISO system such that the pre-distortion cancels distortions caused by the signal path and the combiner combining the signals from the branches into which input signals are input. The method includes generating uncorrelated noises and mixing them with the input signals, evaluating the output of the combiner based on the input signals and the noises in order to determine a respective contribution of each input signal to the output of the combiner, and accordingly determining an appropriate pre-distortion. The signal path may apply a non-linear and/or dynamic function on the signal.