Spectrum Sensing Based on Censored Observations in Time-Varying Channels using AR-1 Model

Non-parametric sensing algorithms are preferred in cognitive radio. In this paper, spectrum sensing method based on censored observations is proposed. We evaluate the performance of Censored Anderson-Darling (CAD) sensing method in time-varying and flat-fading channel using Monte Carlo simulations. We have shown the performance of the CAD sensing in terms of receiver operating characteristic (ROC). The considered channel is modeled by Gaussian variables and characterized by a first ordered autoregressive process ($AR1$). It is shown that the proposed method outperforms prevailing techniques such as the Energy detection (ED) sensing and  Order-statistic (OS) based sensing in time-varying channel at lower signal to noise ratio

Narrowband Cooperative Spectrum Sensing in Cognitive Networks

With the increase of different types of wireless devices, the radio frequency (RF) spectrum will not longer large enough to accommodate these increased devices for communication in the future under the traditional fixed spectrum access (FSA) policy. Therefore, cognitive radio (CR), which provides devices flexible spectrum access, has been proposed to solve this scarcity problem in RF spectrum. The ability of CR depends largely on its spectrum sensing since it provides device access to one spectrum band while avoiding interference to other devices. However, the results from single spectrum sensing is not reliable in real communication condition due to various fading effects. Thus, designing an efficient cooperative spectrum sensing scheme a significant task. In this thesis, two cooperative narrowed spectrum sensing schemes, multi-selective cooperation and selective cooperation, will be proposed. Multi-selective cooperation, an improved version from selection combining (SC), is based on ordered statistics of the reporting links between the cooperative nodes and fusion center where the links with high signal-to-noise ratios (SNRs) are selected as reliable reporting links. Furthermore, we examine the optimum N-out-of-K rule of our scheme under different detection threshold and SNR. Another new scheme, selective-cooperation, is proposed based on multi-selective cooperation and it selects the links, whose SNRs are larger than fusion center's, as reliable reporting links. The performance of both new schemes are compared to other existing schemes in-terms of the probability of detection and probability of false alarm over independent identity distributed (i.i.d) and independent non-identical distributed (i.n.d) Rayleigh fading channels. Both simulations and analytical results show that the multi-selective scheme outperforms some traditional schemes, i.e. selection combining, general N-out-of-K rule and square-law selection (SLS) under different system parameters. Simulations and analytical results also show that the performance of the selective-cooperation scheme gets further improvement compared with multi-selective scheme and it outperforms some traditional schemes, i.e. square-law combining (SLC), under different communication environments

Order-statistic based spectrum sensing for cognitive radio

Spectrum sensing for cognitive radio is challenging. In this letter, a spectrum sensing method based on quintiles of Order-Statistics is proposed. We derive the test statistic and evaluate the performance of the proposed method by Monte Carlo simulations. Simulation results show that order statistics based sensing considerably outperforms both energy detection and anderson darling based sensing in an Additive White Gaussian Noise (AWGN) channel; especially in a lower signal to noise ratio region