Machine Learning Network RF Signal Processing Optimization
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Solution Overview
Problem
Existing technologies face challenges in efficiently training and deploying machine-learning networks for optimal radio frequency (RF) signal processing, particularly in simulating complex RF environments and optimizing network architectures and hyper-parameters for low power consumption and high accuracy.
Innovation Solution
The method involves training a machine-learning network using a first RF signal to produce a signal processing model output, measuring a distance metric between this output and a reference model output, and iteratively modifying the network's operations and hyper-parameters to reduce this distance metric, ultimately determining a score indicating the network's performance in performing desired RF functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning networks are trained to simulate complex RF environments with high accuracy, then the performance and precision of signal processing improve, but the power consumption and computational complexity increase
Solution Approach 1:
The patent applies parameter changes by systematically modifying hyper-parameters (learning rate, batch size, network depth, width, and configuration) to find the optimal balance between accuracy and power consumption. The training process iteratively adjusts these parameters to achieve high signal processing accuracy while minimizing energy usage through efficient network architecture selection.
Solution Approach 2:
The system dynamically adapts the machine-learning network architecture during training by modifying operations and hyper-parameters based on performance feedback. The network structure evolves from initial configuration to optimized architecture, allowing the system to achieve high accuracy with reduced computational complexity in the final deployed model.
2Reliability
If the machine-learning network architecture is optimized for high accuracy in RF signal processing, then the performance improves, but the device complexity and training time increase
Solution Approach 1:
The patent segments the network optimization process into distinct phases: initial network configuration, iterative training with hyper-parameter modification, and final deployment. This segmentation allows complex optimization tasks to be broken down into manageable steps, reducing overall system complexity while maintaining high performance through systematic refinement.
Solution Approach 2:
The system implements feedback mechanisms by measuring the distance metric between signal processing model output and reference model output during training. This feedback drives iterative modifications to network operations and hyper-parameters, enabling the system to automatically optimize architecture complexity while maintaining or improving performance through evidence-based adjustments.
3Manufacturing precision
If iterative modifications are made to reduce the distance metric between model output and reference output, then the training precision improves, but the training time and computational resources increase
Solution Approach 1:
The patent applies partial action by modifying only the necessary operations andhyper-parameters that have the greatest impact on reducing the distance metric, rather than exhaustively searching all possible network configurations. This selective modification approach achieves sufficient model accuracy without requiring excessive training time or computational resources.
Solution Approach 2:
The system performs preliminary actions by establishing an initial network configuration and training framework before iterative optimization begins. Pre-computed reference model outputs and pre-configured hyper-parameter ranges are prepared in advance, enabling efficient iterative refinement without repeated setup overhead, thus reducing total training time while maintaining precision.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication. One of the methods includes: receiving an RF signal at a signal processing system for training a machine-learning network; providing the RF signal through the machine-learning network; producing an output from the machine-learning network; measuring a distance metric between the signal processing model output and a reference model output; determining modifications to the machine-learning network to reduce the distance metric between the output and the reference model output; and in response to reducing the distance metric to a value that is less than or equal to a threshold value, determining a score of the trained machine-learning network using one or more other RF signals and one or more other corresponding reference model outputs, the score indicating an a performance metric of the trained machine-learning network to perform the desired RF function.