Parallel Computation based Spectrum Sensing Implementation for Cognitive Radio
AbstractThe reliably and timely spectrum sensing ability is very critical to cognitive radio. Cyclostationary feature detection has the ability to separate the signal of interest from noise and/or interference, but the computational complexity of cyclic spectral analysis limits its use as a signal analysis tool. To reduce the computational complexity of cyclic spectral analysis, this paper proposes an efficient parallel FFT accumulation method (FAM) algorithm on a novel SDR processor for next generation wireless communication, GAEA. Parallelized cyclostationary feature detection implementation for a common parameter set of spectrum sensing (32768 samples) can be finished within approximately 126 ms on our Lyrtech SDR experiment platform. The algorithm is expandable and can be mapped to more processors to get shorter detection time. This approach is suitable for spectrum sensing of cognitive radio and other cyclostationary feature detection applications. Ill. 6, bibl. 14, tabl. 2 (in English; abstracts in English and Lithuanian).
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