M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.Spectrum Sensing

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    Spectrum sensing in cognitive radio networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2014) Ahir, Sagar J.; Pillutla, Laxminarayana S.
    In this thesis, we consider the problem of secondary users’ selection for cooperative spectrum sensing in cognitive radio networks. We assume that the secondary users involved in sensing transmit their spectral energy measurements to the fusion center. The fusion center performs maximal ratio combining (MRC) before taking a decision on the presence or absence of primary users. MRC reduces the complexity of decision rule and also facilitates in computation of the expressions for probability of detection and false alarm. Due to the discrete nature of the parameter set (which is nothing but the indices of the secondary users) and the fact that we have to work with estimates of the underlying objective function we pose the problem of secondary users’ selection under the discrete stochastic optimization framework. For this purpose we assume the objective function to be the probability of detection with the probability of false alarm set to a desired value. Owing to the high computational complexity associated with the exhaustive search, we propose an algorithm that appeared in operations research literature which spends most of its time near the global optimizer. The algorithm can also naturally track the optimal secondary users subset due to variations in channel gains. We also extend the above optimization framework to even include the case of antenna selection for cases when the secondary users are equipped with multiple antennas

    for sensing. Our simulation results demonstrate the efficacy of the proposed approach.

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    ItemOpen Access
    Multiband cooperative spectrum sensing in cognitive radio networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2013) Pathak, Anusha; Chakka, Vijaykumar
    Spectrum Sensing forms one of the most important functions of Cognitive Radio operation. Energy detection is a less complex spectrum sensing technique which does not need knowledge of Primary User (PU) signal. An Energy detection based Spectrum Sensing technique in frequency domain is discussed, which uses a Likelihood Ratio Test (LRT) as test statistic. Multiband sensing is performed on the received sampled signal at the Cognitive users (CR). The received signal samples are first converted into frequency domain using Discrete Fourier Transform (DFT) using the Fast Fourier Transform (FFT) algorithm. Once in frequency domain the signal power is calculated in each of the sub-bands which is compared against a threshold calculated using the Neyman-Pearson criterion for a fixed Probability of False alarm (Pf ). The Probability Density Function (Pdf) for the power of the signal in each of the subbands has been derived assuming the noise power level and the channel conditions are known to the receiver, using some estimation method. This Pdf is used to calculate the Likelihood ratio under the two hypothesis for signal presence and absence. This likelihood ratio is compared against the above derived threshold and the Primary User presence or absence is declared. Cooperative detection is considered for improving the sensing performance. Cooperation utilizes the spatial diversity among the observations of different CR users. Hard and Soft fusion schemes are compared in terms of complexity and performance.
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    ItemOpen Access
    Low complexity 2-level (FFT-GOERTZEL) spectrum sensing method for cognitive radio
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Bhatt, Prakruti Vinodchandra; Chakka, Vijaykumar
    Energy detection in frequency domain is a preferred technique for the spectrum sensing and the accuracy of frequency estimation depends on the Discrete Fourier Transform (DFT) size. Instead of computing full length (N) DFT of the whole data, a new two level (coarse-fine) technique for energy detection is proposed. In the first (coarse) level, time averaging of smaller size (L<