2013/11/25 by Tadilo Endeshaw Bogale, Bogale, Tadilo Endeshaw, Luc Vandendorpe +1
Computer Science · Neuroscience · #Blind Source Separation Techniques #Cognitive Radio Networks and Spectrum Sensing #Distributed Sensor Networks and Detection Algorithms #EEG and Brain-Computer Interfaces #FOS: Mathematics #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.1311.6388
openalex publication_date 2013/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper proposes novel spectrum sensing algorithms for cognitive radio\nnetworks. By assuming known transmitter pulse shaping filter, synchronous and\nasynchronous receiver scenarios have been considered. For each of these\nscenarios, the proposed algorithm is explained as follows: First, by\nintroducing a combiner vector, an over-sampled signal of total duration equal\nto the symbol period is combined linearly. Second, for this combined signal,\nthe Signal-to-Noise ratio (SNR) maximization and minimization problems are\nformulated as Rayleigh quotient optimization problems. Third, by using the\nsolutions of these problems, the ratio of the signal energy corresponding to\nthe maximum and minimum SNRs are proposed as a test statistics. For this test\nstatistics, analytical probability of false alarm (Pf) and detection (Pd)\nexpressions are derived for additive white Gaussian noise (AWGN) channel. The\nproposed algorithms are robust against noise variance uncertainty. The\ngeneralization of the proposed algorithms for unknown transmitter pulse shaping\nfilter has also been discussed. Simulation results demonstrate that the\nproposed algorithms achieve better Pd than that of the Eigenvalue\ndecomposition and energy detection algorithms in AWGN and Rayleigh fading\nchannels with noise variance uncertainty. The proposed algorithms also\nguarantee the desired Pf(Pd) in the presence of adjacent channel\ninterference signals.\n