Spectrum Sensing Function Using Higher-Order Statistics
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Solution Overview
Problem
Current signal detection methods in wireless communication systems face challenges in accurately detecting signals buried in additive white Gaussian noise, especially at low signal-to-noise ratios, and struggle to efficiently utilize the radio frequency spectrum due to inefficient band allocation.
Innovation Solution
The implementation of a Spectrum Sensing Function using higher-order statistics (HOS) for signal detection in both time and frequency domains, involving steps like band-pass filtering, down-conversion, analog-to-digital conversion, and Fast Fourier Transform (FFT) processing, allows for efficient detection of various signal types, including ATSC DTV and wireless microphone signals, even at low SNRs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional energy detection methods are used, then the system is simple to implement, but the detection accuracy deteriorates at low signal-to-noise ratios
Solution Approach 1:
The patent changes the detection parameter from energy (second-order statistic) to higher-order statistics (third-order and above). This parameter transformation enables the system to detect non-Gaussian signal characteristics that remain visible even in low-SNR conditions, resolving the contradiction between implementation simplicity and detection accuracy.
Solution Approach 2:
The patent substitutes traditional energy detection mechanisms with higher-order statistic-based detection. By replacing the simple energy measurement approach with complex higher-order moment calculations, the system achieves superior detection accuracy while maintaining reasonable implementation complexity through algorithmic optimization.
2Measurement precision
If higher-order statistics are used for detection, then the detection accuracy improves at low SNR, but the computational complexity increases
Solution Approach 1:
The patent segments the detection process into distinct stages: signal collection, higher-order statistic computation, threshold evaluation, and decision making. This segmentation allows each component to be optimized independently, reducing overall computational burden while maintaining high detection accuracy through targeted processing of signal characteristics.
Solution Approach 2:
The patent computes higher-order statistics selectively rather than exhaustively. By calculating only the necessary higher-order moments (third-order and above) and using them in a structured decision framework, the system achieves accurate detection without requiring full computational analysis of all possible signal characteristics.
3Ease of manufacture
If the spectrum is allocated inefficiently, then the system design is simple, but the spectrum utilization deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where detected signal characteristics and spectrum usage information are fed back to the cognitive radio system. This feedback enables dynamic spectrum allocation decisions, allowing the system to adapt to changing spectral conditions and optimize utilization without requiring complex predetermined allocation schemes.
Solution Approach 2:
The patent introduces dynamic spectrum allocation capabilities that adapt to real-time spectral conditions. By making the allocation strategy flexible and responsive to detected signal patterns, the system optimizes spectrum usage efficiency while maintaining design simplicity through algorithmic rather than structural complexity.
Data Source
AI summary
A method and system are disclosed to detect a broad class of signals including Advanced Television Systems Committee (ATSC) digital television (DTV) and wireless microphone signals. This signal detection method performs in Gaussian noise, employing Higher Order Statistics (HOS). Signals are processed in time and frequency domains as well as by real and imaginary components. The spectrum sensing employed also supports Denial of Service (DoS) signal classification. The method can include parameters that may be tailored to adjust the probability of detection and false alarm.