Compressive Sensing With Optimal Sparsifying Basis and Applications in Spectrum Sensing

The authors describe a method of integrating Karhunen-Loeve Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. They present two complementary results: by using KLT to find an optimal basis for decoding they can drastically reduce the number of measurements for compressive sensing used in applications such as radio spectrum analysis; by using compressive sensing they can estimate and recover the KLT basis from compressive measurements of an input signal. In particular, they propose CS-KLT, an online estimation algorithm to cope with non-stationarity of wireless channels in reality.

Provided by: Harvard University Topic: Mobility Date Added: Aug 2012 Format: PDF

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