On the energy detection of unknown deterministic signal over Nakagami channelswith selection combining

Herath, Sanjeewa P.; Rajatheva, Nandana; Tellambura, Chintha

doi:10.1109/ccece.2009.5090228

Cited by 60 publications

(50 citation statements)

References 8 publications

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“…Another common option is the cyclostationary detector, which has a much higher complexity and requires knowledge of the cyclic frequencies [15], which again is a quantity that is not easily available. The major drawback of ED is that it requires knowledge of the noise-power [8,9,16,17,19,22,24]. Lack of this knowledge or noise-power uncertainty [8,9,15,18,19,25,26] causes a signal-to-noise ratio (SNR) wall and a high PFA.…”

Section: Introductionmentioning

confidence: 99%

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Blind eigenvalue-based spectrum sensing for cognitive radio networks

Pillay

2012

IET Commun.

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Spectrum sensing for cognitive radio allows a secondary user to detect spectrum 'holes' and to opportunistically exploit this space for unlicensed communication. Blind spectrum sensing has the advantage that it does not require any knowledge of the transmitted signal, the channel or the noise-power, which are usually unknown at the receiver. In this study, the simulation and performance results for the maximum -minimum-eigenvalue and energy-minimum-eigenvalue sensing methods are presented for the Nakagami-m fading channel. The simulation and performance results are presented for the maximum-eigenvalue-to-trace method and the arithmetic-to-geometric-mean method together with the analytical expressions for the threshold, probability of detection and probability of false alarm. In addition, another algorithm, maximum-eigenvalue-geometric-mean is proposed and is investigated in terms of the analytical and simulation results for Nakagami-m fading channels. Improved performance is shown compared to the other schemes when the number of samples is decreased and when the number of cooperating users is increased such that the ratio of the latter to the former is positive and less than unity. Analytical expressions are also presented. The eigenvalue detection methods exhibit good performance in noisy environments and are matched by their bounds.

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Section: Introductionmentioning

confidence: 99%

“…We assume g i = g for identical channels. For Nakagami-m fading channels the probability density function (PDF) of g i is defined in [20,24]. We also assume that there is no line of sight present.…”

Section: Introductionmentioning

confidence: 99%

Blind eigenvalue-based spectrum sensing for cognitive radio networks

Pillay

2012

IET Commun.

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“…Based on [5], the ROC analysis over Rayleigh, Rician, Nakagami-m, and η-μ channel models is discussed in [6]- [9]. Furthermore, diversity reception techniques such as equal gain combining (EGC), selection combining (SC), maximal ratio combining (MRC), square law combining (SLC), and square law selection (SLS), which are used to boost the performance of the energy detector, are discussed in [7]- [11]. The ROC analysis reveals that MRC improves the performance of the energy detector the most among all the diversity reception techniques.…”

Section: Introductionmentioning

confidence: 99%

Performance of Energy Detection: A Complementary AUC Approach

Atapattu

Tellambura

Jiang

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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This paper 1 investigates detection capability of energy detectors. With the help of receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), a new measure, Complementary AUC (CAUC), is introduced as a proxy for the overall detection capability. When relays are available to help forward the target signal, the upper bound of the CAUC under Rayleigh fading channels is derived without and with a direct path. In addition, the average CAUC is discussed for Nakagami-m fading channels without and with diversity combining. The analytical results are validated by numerical examples.Index Terms-Area under the curve, energy detection, receiver operating characteristics (ROC), relay.

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“…MGC-SDF is the optimal choice; however, this scheme requires information between the SU and the PU, which is difficult to get in practice. The MGC-SDF is also sensitive to the attack of MUs sending false data to the FC [28][29][30]. The CSS protection scheme, discussed in [31], is used for the detection of the always yes and always no MUs, where outlier detection technique is used.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with [39], this work optimizes the detection, false alarm, and error results when MUs take low and high SNR of the channel in comparison with NSUs. The proposed scheme is tested at different levels of SNR and increasing number of cooperative users with simple soft decision fusion (SDF) and hard decision fusion (HDF) schemes in [28][29][30][31][32][33][34][35][36]. Simulation results at different levels of cooperative SUs and various SNR levels confirmed that with the use of the one-to-many neighbor distance-and z-score-based GA, the system is able to produce more sophisticated detection results for the HDF schemes in the presence of MUs.…”

Section: Introductionmentioning

confidence: 99%

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Gul

Qureshi

Elahi

et al. 2018

International Journal of Antennas and Propagation

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In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.

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On the energy detection of unknown deterministic signal over Nakagami channelswith selection combining

Cited by 60 publications

References 8 publications

Blind eigenvalue-based spectrum sensing for cognitive radio networks

Blind eigenvalue-based spectrum sensing for cognitive radio networks

Performance of Energy Detection: A Complementary AUC Approach

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

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