Sample-by-Sample Adaptive Controller for Under-Determined Wireless Systems
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Solution Overview
Problem
Adaptive systems for under-determined systems in wireless base stations face inefficiencies and robustness issues due to high correlation in excitation signals, leading to poor convergence rates, parameter drift, and excess errors, especially in complex model structures and high sample rates.
Innovation Solution
The implementation of a sample-by-sample adaptive controller with pre-conditioning of inputs, constraining of adaptive elements, and modification of internal adaptation mechanics, allowing for improved adaptation performance without altering the input or controlled signal, and being agnostic to bandwidth and model complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If block-based processing is used to solve under-determined systems, then model parameters can be solved directly, but convergence rate deteriorates and system down-time increases
Solution Approach 1:
The patent divides the block-based processing into hierarchical levels: outer block-based adaptation cycles that periodically update model parameters, and inner sample-by-sample adaptation that continuously refines parameters between blocks. This segmentation allows direct solution accuracy at block boundaries while maintaining continuous convergence through sample-by-sample updates.
Solution Approach 2:
The system implements periodic block-based adaptation cycles where full model parameter solutions are computed at intervals, combined with continuous sample-by-sample parameter updates between blocks. This periodic re-solution maintains accuracy while the continuous updates ensure steady convergence rate.
2Productivity
If gradient methods are used for sample-by-sample adaptation, then continuous parameter adjustment is achieved, but robustness deteriorates due to signal correlation
Solution Approach 1:
The patent implements a feedback mechanism where the block-based solution provides reference accuracy that guides and corrects the sample-by-sample gradient updates. The error between block-based and sample-by-sample results feeds back to adjust the adaptation step size and direction, improving robustness against signal correlation while maintaining continuous adaptation.
Solution Approach 2:
The system performs preliminary block-based model parameter solutions before entering sample-by-sample adaptation modes. This preliminary action establishes accurate initial conditions and constraints that make the subsequent gradient-based sample-by-sample adaptation more robust to signal correlation issues.
3Manufacturing precision
If model complexity is increased to improve solution accuracy, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The patent implements dynamic model complexity management where the model dimensionality and span are adjusted based on the degree of under-determinedness detected in the system. When signal correlation is high or bandwidth is narrow, the model complexity is automatically reduced to maintain robustness, while allowing higher complexity when conditions permit for improved accuracy.
Solution Approach 2:
The system dynamically changes model parameters including dimensionality, span, and regularization coefficients based on operating conditions such as signal bandwidth, sample rate, and correlation properties. This allows the model to adapt its complexity to match the information available in the excitation signal.
4Measurement precision
If characterization mode is used for model training, then model accuracy improves, but system down-time increases
Solution Approach 1:
The patent eliminates system down-time for characterization by implementing continuous adaptation that operates throughout normal transmission. The sample-by-sample gradient methods allow model parameters to be continuously refined during actual signal transmission, while periodic block-based solutions maintain accuracy without requiring separate training periods.
Data Source
AI summary
A device and method to adapt a model in a underdetermined adaptive system that provides an output in response to an input. A controller provides parameters to the model in a transceiver system, composed of linearizers, equalizers, or estimators as a function of an error signal. The controller and the model parameters are manipulated to allow agnosticism with respect to input signals or model complexity, enabling robust operation and efficient implementation.