Sliding Window Energy Detection for Low SNR Spectrum Sensing
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Solution Overview
Problem
Existing spectrum sensing methods using energy detection fail to accurately account for correlations among detection tests in sliding window energy detection, leading to inefficiencies in false alarm rate evaluation and detection probability analysis.
Innovation Solution
A method for spectrum sensing using sliding window energy detection that determines a desired cumulative false alarm rate, calculates a single-time false alarm rate, and obtains a testing threshold from an independent energy detection test to optimize the sliding window energy detection process, incorporating a sampling unit, detection probability analyzer, testing statistic generator, false alarm analyzer, and declaring unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sliding window energy detection is used for spectrum sensing, then detection speed and continuity are improved, but correlations among detection tests cause inaccurate false alarm rate evaluation
Solution Approach 1:
The patent introduces an intermediary mapping relationship between sliding window energy detection false alarm rates and independent energy detection false alarm rates. This mapping acts as a mediator that allows accurate evaluation of correlated detection tests by transforming them into equivalent independent test parameters, thereby resolving the measurement precision issue while maintaining the speed benefits of sliding window detection.
Solution Approach 2:
The patent changes the parameter representation from direct sliding window false alarm rates to equivalent independent energy detection false alarm rates through the mapping relationship. This parameter transformation enables accurate evaluation of the correlated detection tests by converting them into a form that can be handled by established independent test theory, thus improving measurement precision without sacrificing detection speed.
2Device complexity
If independent energy detection tests are assumed for sliding window detection, then analysis simplicity is improved, but detection probability and false alarm rate become inaccurate
Solution Approach 1:
The mapping relationship serves as an intermediary that bridges the gap between simple independent test analysis and the complex correlated sliding window detection. It allows the system to maintain the simplicity of independent test analysis while achieving the reliability of correlated detection evaluation by transforming the problem into an equivalent independent test framework.
Solution Approach 2:
The patent creates a copy of the independent energy detection test framework that can be used to analyze sliding window detection performance. By copying the independent test model and adapting it through the mapping relationship, the system achieves accurate detection probability and false alarm rate evaluation while maintaining the simplicity of the original independent test analysis approach.
3Reliability
If window length is increased to improve detection accuracy, then detection probability improves, but detection time increases
Solution Approach 1:
The patent makes the window length parameter dynamic by optimizing it based on the mapping relationship between sliding window and independent energy detection parameters. This dynamic adjustment allows the system to achieve high detection probability with minimal detection time by selecting the optimal window length that balances these two competing requirements under the given detection conditions.
Solution Approach 2:
The patent optimizes the window length parameter by changing it based on the mapping relationship and detection requirements. Through this parameter optimization, the system achieves the best possible detection probability while minimizing detection time, resolving the contradiction between accuracy and speed by finding the optimal balance point.
Data Source
AI summary
Methods and devices for spectrum sensing using sliding window energy detection are provided. A sliding window energy detection test having a number of continuously-performed tests can be analyzed according to a desired cumulative false alarm rate to provide a corresponding, testing threshold. Based on the testing threshold and target signal to noise ratio, a testing window length is selected such that the sliding window energy detection is performed for a minimum expected discrete detection time. A sliding window energy detector can then obtain the selected testing window length and the corresponding, testing threshold for spectrum sensing. The sliding window energy detector includes a sampling unit, a detection probability analyzer, a testing statistic generator, a false alarm analyzer, a comparing unit, and a declaring unit.