Fast Sequency-Ordered Complex Hadamard Transform-Based Parzen Window Entropy Detection for Spectrum Sensing in Cognitive Radio Networks

Abstract

Spectrum sensing is accomplished at the physical layer of cognitive radio networks. This letter presents a fast sequency-ordered complex Hadamard transform (FSCHT)-based Parzen window entropy detection technique (PWED) for spectrum sensing. The energy compaction property of FSCHT leads to a discriminating sensing performance compared to fast Fourier transform (FFT) transform. In PWED, the kernel-based probability density estimation is employed to evaluate the entropy. The impact of orthogonal transforms on the computation of entropy is analyzed. The computational complexity of PWED technique is compared with Shannon entropy technique. A substantial improvement in the SNR wall is observed in the presence of noise uncertainty. The proposed technique detects the DVB-T signal up to-54 dB SNR with probability of detection (Pd) 0.9 and probability of false alarm (Pfa) 0.1.