IIIT Hyderabad Publications |
|||||||||
|
Cooperative Spectrum Sensing using Heterogeneous Sensors and Copula TheoryAuthor: Akhil Singh Date: 2020-04-11 Report no: IIIT/TH/2020/17 Advisor:Sachin Chaudhari AbstractCurrent mobile technology does not utilize the spectrum efficiently. This problem is going to increase in future because of increase in wireless devices. Cognitive Radio (CR) is one of the solutions to exploit the underutilized spectrum. It is key enabler for next generation mobile technology 5G with applications such as efficient spectrum utilization and interference management. Spectrum sensing is an important component of cognitive radio networks (CRNs) as it provides spectrum awareness required for cognitive processing. In this thesis, we first investigate a distributed and heterogeneous CRN, comprising of secondary users (SUs) employing either energy detector (ED) or autocorrelation detector (AD) to detect the pres- ence or absence of an orthogonal frequency division multiplexing (OFDM) based primary user (PU). For the considered heterogeneous cooperative spectrum sensing (CSS), the optimal soft combining rule is derived. The performance of this optimal fusion rule and different hard combining schemes such as OR, AND and MAJORITY is presented for the case when the noise variance is exactly known. Later, the effect of noise uncertainty is also presented. The proposed heterogeneous CSS is shown to com- bine the excellent performance of the EDs (when the noise variance is exactly known) and robustness of the ADs to the noise uncertainty. However, the proposed soft combining based fusion rule assumes independence between the observations which may not hold in practical scenarios. Incorporating dependence between observations in fusion rule for CSS scheme forced us to look for alternative concepts and theories beyond standard concepts used in the context of spectrum sensing. Copula theory provides a new dimension to the picture. It enables us to incorporate dependence in the fusion process. Using the concept of copula theory, we forged a novel copula-based fusion rule for spectrum sensing. We investigate a distributed detection model where SUs employ ADs for the detection of a PU. In the presence of a PU, it is assumed that the observations across different SUs and subsequently the decision statistics are dependent. For the fusion of these dependent statistics, different copulas such as t-copula, Gaussian, Clayton and Gumbel are employed. In the presence of dependence among decision statistics, significant improvement in detection performance is observed while using copula theory instead of the traditional assumption of independence. Simulation results are presented to show the superiority of copula-based spectrum sensing. Full thesis: pdf Centre for Others |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |