AI Signal Analysis Module for Automated Measurement Setup
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current signal analysis methods require manual setting of operational modes based on unknown signal properties, which is time-consuming and requires detailed knowledge, limiting their usability to experienced users.
Innovation Solution
A signal analysis method utilizing artificial intelligence to automatically determine characteristic signal parameters and adapt measurement instrument settings, employing machine learning techniques to analyze IQ data and set the instrument to the correct operational mode without manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting of operational mode is used, then measurement precision can be maintained, but ease of operation deteriorates and requires detailed user knowledge
Solution Approach 1:
The system automatically determines signal parameters and selects operational modes without user intervention. The measurement instrument performs self-configuration by analyzing the input signal characteristics and autonomously setting the appropriate measurement parameters, eliminating the need for manual user setup while maintaining accuracy.
Solution Approach 2:
The system dynamically changes measurement parameters based on automatic signal analysis. By continuously monitoring signal characteristics and adjusting operational modes accordingly, the system adapts parameters such as bandwidth, sampling rate, and measurement type automatically, ensuring precision without requiring user knowledge of parameter settings.
2Measurement precision
If manual signal property determination is used, then measurement accuracy can be ensured, but loss of time increases due to time-consuming procedures
Solution Approach 1:
The system performs preliminary automatic analysis of signal properties before the actual measurement process. By pre-determining signal characteristics such as modulation type, bandwidth, and power level through automated algorithms, the system prepares the optimal measurement configuration in advance, eliminating time-consuming manual determination steps while ensuring accurate measurements.
Solution Approach 2:
The system replaces manual mechanical procedures with automated electronic signal analysis. Instead of requiring users to manually adjust controls and determine signal properties, the system uses digital signal processing and automated algorithms to rapidly analyze signal characteristics and configure measurement parameters, dramatically reducing analysis time while maintaining precision.
3Ease of operation
If automated parameter determination is implemented, then ease of operation improves, but device complexity increases due to AI circuitry requirements
Solution Approach 1:
The AI circuitry is integrated into the existing signal processing architecture of the measurement instrument, serving multiple functions including signal characterization, parameter determination, and operational mode selection. By making the AI module multi-functional and integrating it with existing hardware resources, the system achieves automated operation without proportionally increasing overall device complexity.
Solution Approach 2:
The AI circuitry acts as an intermediary layer between the raw signal input and the measurement processing system. It automatically translates complex signal characteristics into simplified operational parameters that the existing measurement instrument can process, bridging the gap between automatic determination capabilities and traditional measurement functions without requiring complete system redesign.
4Ease of operation
If user knowledge requirements are reduced, then ease of operation improves, but measurement precision may deteriorate without proper signal property knowledge
Solution Approach 1:
The system performs self-characterization by automatically analyzing signal properties and determining optimal measurement parameters without user input. The measurement instrument autonomously identifies signal type, estimates bandwidth, and selects appropriate measurement modes, ensuring precision is maintained through automated expert-level analysis rather than user knowledge.
Solution Approach 2:
The system implements feedback loops where measurement results are continuously monitored and used to refine parameter determination. By comparing initial automatic parameter estimates with actual measurement outcomes, the system iteratively optimizes settings to ensure precision, compensating for the lack of user expertise through automated feedback-driven adjustment.
Data Source
AI summary
A signal analysis method is described. The signal analysis method includes: receiving an input signal having unknown characteristic signal parameters; determining IQ data being associated with the input signal; determining at least one of the characteristic signal parameters based on the IQ data via an artificial intelligence circuit; and adapting at least one measurement parameter of a measurement instrument based on the at least one characteristic parameter by the artificial intelligence circuit. Moreover, a signal analysis circuit is described.