Enhancement of spectrum sensing techniques in cognitive radio

Abstract

The spectrum scarcity faced in cognitive networks due to day to day increase in radio traffic needs to be addressed for effective usage of available spectrum. More efficient spectrum-sensing methods may be implemented to identify the under-utilized radio frequency bands to handle the excessive radio traffic. Conventional cognitive spectrum sensing methods have been reported to have degraded performance under different environments including less signal to noise ratio (SNR), fading and shadowing. This may result in rise of instants of probability of false alarm (Pf), minimum probability of actual detection (Pd), and misdetection (Pmis) of hidden terminal. An attempt has been made to cater to these limitations by using a dynamic selection of threshold and efficient power spectral estimation techniques for spectrum sensing. The primary objective was to implement an energy detection-based transmitter section. A more efficient primary signal detection technique has been developed by comparing the selected threshold with the computed energy level of the signal. This has been followed by the determination of unused frequency bands. The performance has been evaluated by considering short, medium, and long character length modulated messages (using binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK)) as input. For performance evaluation of the implemented spectrum sensing technique has been done by computing SNR, Pf, and Pd. The results highlighted that the selection of adequate modulation techniques may enhance the usage of the spectrum. The next phase of the research consists of the analysis of the double dynamic threshold with the received signal energy for improved spectrum sensing followed by the hybrid spectrum sensing technique introduced for more efficient identification of the spectrum holes across a wide range of SNRs. All simulations have been performed in the MATLAB workspace. The performance has been analyzed using quadrature amplitude modulation (QAM) and BPSK modulated signal. A better