EMD-Based Spectrum Sensing for Low SNR Detection

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Solution Overview

Problem

Current spectrum sensing techniques in communication systems face challenges in identifying available spectrum efficiently, especially at low signal-to-noise ratios, and require high computational complexity, which hinders effective operation in cognitive radio systems.

Innovation Solution

The implementation of an Empirical Mode Decomposition (EMD)-based energy detector that operates on non-stationary and non-linear signals without prior information, using intrinsic mode functions (IMFs) to differentiate between occupied and available spectrum, and adapts to noise conditions by calculating a data-driven threshold.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If traditional spectrum sensing techniques are used, then measurement precision may be maintained, but device complexity and computational complexity increase significantly

Engineering Contradiction:

Improvecomputational complexityVSAvoidspectrum detection accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent applies segmentation by decomposing the received signal into multiple Intrinsic Mode Functions (IMFs) through Empirical Mode Decomposition. This breaks down the complex signal analysis problem into simpler components, where each IMF represents a specific frequency band. By analyzing energy distribution across these segmented IMFs rather than processing the entire signal at once, the computational complexity is significantly reduced while maintaining detection accuracy.

Inventive Principle:

Principle #1Segmentation

2Reliability

If traditional spectrum sensing techniques are used, then measurement precision may be maintained, but the system cannot effectively operate at low signal-to-noise ratios

Engineering Contradiction:

Improveperformance at low SNRVSAvoidspectrum identification accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent changes the analysis parameter from traditional fixed-frequency spectrum analysis to adaptive time-frequency analysis using IMFs. By transforming the signal into the time-frequency domain through EMD and analyzing energy distribution across different IMF components, the system can effectively detect signals at low SNR ratios. The data-driven threshold calculation further adapts to noise conditions, maintaining measurement precision in challenging low-SNR environments.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If complex spectrum sensing techniques are used, then measurement precision is improved, but ease of operation deteriorates

Engineering Contradiction:

Improveease of spectrum identificationVSAvoidspectrum sensing accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent implements self-service through automatic threshold calculation based on the statistical properties of the IMFs. The system automatically determines the threshold for distinguishing signal from noise using the energy distribution characteristics of the decomposed IMFs, without requiring manual intervention or prior knowledge of signal parameters. This self-adjusting mechanism simplifies operation while maintaining high measurement precision across varying signal conditions.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS9565040B2Empirical mode decomposition for spectrum sensing in communication systems

Publication Date: 2017.02.07 UNIVERSITY OF NEW HAMPSHIRE

AI summary

A system and method using an Empirical Mode Decomposition (EMD)-based energy detector for spectrum sensing in a communication system. The EMD energy detector needs no prior information of the received signal, has relatively low computational complexity, operates on non-stationary and non-linear signals, and performs well at low SNR.