Parallel Signal Detection in Data Streams
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional signal detection methods in data streams, such as RF signals, are resource-intensive and prone to inaccurate results due to repeated application of analytical methods, leading to inefficient processing and potential errors.
Innovation Solution
A combined approach using both analytical and machine learning algorithms, specifically an artificial neural network, is employed to detect signals in data streams, where an analytical signal detection algorithm and a machine learning signal detection algorithm operate in parallel to enhance detection accuracy and efficiency by selecting and combining results based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analytical signal detection algorithms are applied repeatedly to every new data stream, then signal detection can be performed, but the processing becomes resource-intensive and results become inaccurate
Solution Approach 1:
The system performs preliminary action by training machine learning models in advance on representative data streams to learn signal patterns. During actual detection, the pre-trained models quickly process new data streams, avoiding the need to repeatedly apply complex analytical algorithms from scratch and thereby improving both accuracy and efficiency.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw data stream and the final detection result. It processes the input data and produces enhanced detection results that are then combined with analytical algorithm results, improving overall accuracy while reducing the computational burden of repeated analytical processing.
2Reliability
If only analytical algorithms are used for signal detection, then the detection process is straightforward, but the results are inaccurate and resource-intensive
Solution Approach 1:
The system merges machine learning-based detection results with analytical algorithm-based detection results through a combination module. This integration leverages the pattern recognition capabilities of machine learning while maintaining the interpretability of analytical methods, achieving improved reliability without proportionally increasing resource consumption.
Solution Approach 2:
The system applies partial action by using machine learning models selectively for complex detection scenarios where analytical algorithms struggle, rather than applying exhaustive analytical processing to all data streams. This approach improves reliability for difficult cases while avoiding unnecessary resource consumption for simpler cases.
3Measurement precision
If machine learning algorithms are used alone for signal detection, then detection accuracy can be improved, but the system becomes more complex
Solution Approach 1:
The system segments the detection task into two independent parallel pathways: one using machine learning algorithms and the other using analytical algorithms. Each pathway processes data independently and produces separate results that are later combined, allowing the system to leverage machine learning accuracy while maintaining modularity and manageable complexity through clear separation of functions.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A device for combined detection of a signal in a data-stream supplied to the device is provided. The device has a computing module which is configured to, in a first queue, run, in increments of timeslots, an analytical signal detection algorithm on the data-stream to obtain a first signal detection result. The computing module is further configured to, in a second, parallel queue, run in increments of timeslots, a machine learning signal detection algorithm on the data-stream to obtain a plurality of signal detection results and to select a second signal detection result out of the plurality of signal detection results based on a selection criterion. The computing module is also configured to determine a compared signal detection result based on the first result, the second result, and a comparison criterion, and combine a plurality of consecutive compared results to detect the signal in the data-stream supplied.