Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms

Area of Science:

• Electrical Engineering
• Signal Processing
• Wireless Communications

Background:

• Wideband spectrum sensing faces challenges due to high sampling rates required by traditional methods.
• Cognitive radio and spectrum surveillance demand efficient wideband sensing techniques.

Purpose of the Study:

• To address high sampling rate challenges in wideband spectrum sensing using compressed sensing.
• To analyze the impact of data reduction from modulated wideband converters on sparse reconstruction algorithms.
• To develop data reduction invariant reconstruction algorithms for improved spectrum sensing.

Main Methods:

• Utilized a compressed sensing approach with a sub-Nyquist sampling scheme (modulated wideband converter).
• Analyzed the performance of sparse reconstruction algorithms (Orthogonal Matching Pursuit, LASSO) after data reduction.
• Developed a new mathematical framework to prove algorithm invariance to data reduction.
• Introduced a data reduction invariant version of the LASSO algorithm.

Main Results:

• Demonstrated that greedy reconstruction algorithms are invariant to the proposed data reduction.
• Introduced a data reduction invariant LASSO algorithm.
• Achieved good wideband spectrum sensing reconstruction results using both synthetic and measured data.

Conclusions:

• Compressed sensing with modulated wideband converters offers an effective solution for wideband spectrum sensing.
• The proposed invariant reconstruction algorithms maintain accuracy despite data reduction, enhancing efficiency.
• The approach is validated for practical wideband spectrum sensing scenarios.