Predistortion Hardware Configuration via Neural Architecture Search
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Solution Overview
Problem
Power amplifiers in RF systems face challenges in achieving linearity due to their nonlinear behavior, especially at high power levels, leading to distortion and efficiency trade-offs, and existing digital predistortion techniques struggle with accuracy and complexity, especially with increasing sampling rates.
Innovation Solution
The use of model architecture search techniques, such as differentiable neural architecture search, to optimize the configuration of digital predistortion hardware, allowing for the development of parameterized models that can accurately predict and correct nonlinear distortions in power amplifiers, thereby improving linearity and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital predistortion is applied to enhance power amplifier linearity, then linearity improves, but device complexity increases
Solution Approach 1:
The patent applies neural architecture search to automatically optimize the parameters and architecture of predistortion models, transforming the manual design process into an automated parameter optimization process. This resolves the contradiction by finding optimal model configurations that achieve high linearity with reduced complexity through machine learning-driven parameter tuning.
Solution Approach 2:
The patent uses software-based predistortion models that replicate the nonlinear behavior of power amplifiers through computational algorithms rather than complex hardware circuits. By copying the amplifier's nonlinear characteristics in the digital domain, the system achieves linearity correction without requiring complex analog hardware modifications.
2Measurement precision
If higher sampling rates are used in digital predistortion, then accuracy improves, but processing requirements and complexity increase
Solution Approach 1:
The patent employs adaptive neural network models that dynamically adjust their processing based on input signal characteristics. The model architecture search optimizes the computational structure to process high sampling rate signals efficiently by adapting the model complexity to match the actual signal requirements, thereby maintaining accuracy while reducing unnecessary processing overhead.
Solution Approach 2:
The patent divides the predistortion processing into multiple neural network layers with specialized functions, where each layer handles specific aspects of the signal correction. This segmentation allows the system to process high sampling rate signals by distributing the computational load across multiple specialized sub-processes rather than requiring a single complex processing stage.
3Object-generated harmful factors
If more complex predistortion models are used, then distortion reduction improves, but hardware resource consumption increases
Solution Approach 1:
The patent implements self-service through automated neural architecture search that autonomously identifies the optimal model complexity required for a given application. The system automatically determines the minimum necessary model complexity to achieve acceptable distortion reduction, preventing over-engineering and reducing hardware resource consumption by eliminating unnecessary computational overhead.
Data Source
AI summary
Systems, devices, and methods related to using model architecture search for hardware configuration are provided. A method includes receiving, by a computer-implemented system, information associated with a pool of processing units; receiving, by the computer-implemented system, a data set associated with a data transformation operation; training, based on the data set and the information associated with the pool of processing units, a parameterized model associated with the data transformation operation, where the training includes updating at least one parameter of the parameterized model associated with configuring at least a subset of the processing units in the pool; and outputting, based on the training, one or more configurations for at least the subset of the processing units in the pool.