Demonstration of Spectrum Sensing with Blindly Learned Feature

TL;DR

This paper presents a blind feature learning algorithm for spectrum sensing in cognitive radio, demonstrating improved detection performance through hardware experiments using learned eigenvector features.

Contribution

Introduces a novel blind feature learning algorithm and a feature template matching method for spectrum sensing, validated through hardware implementation.

Findings

FTM outperforms blind detector by about 3 dB in detection performance

Features can be learned blindly using eigenvector-based methods

Hardware experiments confirm the effectiveness of the proposed algorithms

Abstract

Spectrum sensing is essential in cognitive radio. By defining leading \textit{eigenvector} as feature, we introduce a blind feature learning algorithm (FLA) and a feature template matching (FTM) algorithm using learned feature for spectrum sensing. We implement both algorithms on Lyrtech software defined radio platform. Hardware experiment is performed to verify that feature can be learned blindly. We compare FTM with a blind detector in hardware and the results show that the detection performance for FTM is about 3 dB better.