Spectrum Sensing Based on Blindly Learned Signal Feature

Details Spectrum Sensing Based on Blindly Learned Signal Feature Spectrum Sensing Based on Blindly Learned Signal Feature

2011-02-14

arxiv.org/abs/1102.2840v2

Computer Science

Information Theory

Scientific paper

Spectrum sensing is the major challenge in the cognitive radio (CR). We propose to learn local feature and use it as the prior knowledge to improve the detection performance. We define the local feature as the leading eigenvector derived from the received signal samples. A feature learning algorithm (FLA) is proposed to learn the feature blindly. Then, with local feature as the prior knowledge, we propose the feature template matching algorithm (FTM) for spectrum sensing. We use the discrete Karhunen--Lo{\`e}ve transform (DKLT) to show that such a feature is robust against noise and has maximum effective signal-to-noise ratio (SNR). Captured real-world data shows that the learned feature is very stable over time. It is almost unchanged in 25 seconds. Then, we test the detection performance of the FTM in very low SNR. Simulation results show that the FTM is about 2 dB better than the blind algorithms, and the FTM does not have the noise uncertainty problem.

Affiliated with

Also associated with

No associations

Rating

Spectrum Sensing Based on Blindly Learned Signal Feature does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Spectrum Sensing Based on Blindly Learned Signal Feature, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Spectrum Sensing Based on Blindly Learned Signal Feature will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-216707