Area under ROC curve of energy detection over generalized fading channels

Bagheri, Alireza; Sofotasios, Paschalis C.; Tsiftsis, Theodoros A.; Shahzadi, Ali; Freear, Steven; Valkama, Mikko

doi:10.1109/pimrc.2015.7343380

Cited by 8 publications

(4 citation statements)

References 40 publications

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“…We must note that in order to evaluate the average AUC, only a finite number of elementary functions is needed; thus, the expression for the average AUC here obtained is of similar complexity to the ones obtained for the Rician scenario [22], and simpler than the ones obtained in [27] for Hoyt fading, which are particular cases of our result. When compared to the available results for generalized fading channels [28][29][30] such as κ-µ, η-µ and α-µ, they are all given in terms of a finite summation. However, the evaluation of hypergeometric or Meijer-G functions is not required in our case unlike when considering η-µ or α-µ fading and u ∈ N.…”

Section: Area Under the Roc Curvementioning

confidence: 99%

Generalized MGF of Beckmann Fading With Applications to Wireless Communications Performance Analysis

Peña-Martín

Romero-Jerez

López‐Martínez

2017

IEEE Trans. Commun.

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Abstract-The Beckmann distribution is a general multipath fading model for the received radio signal in the presence of a large number of scatterers, which can thence be modeled as a complex Gaussian random variable where both the inphase and quadrature components have arbitrary mean and variance. However, the complicated nature of this distribution has prevented its widespread use and relatively few analytical results have been reported for this otherwise useful fading model. In this paper, we derive a closed-form expression for the generalized moment-generating function (MGF) of the signal-to-noise ratio (SNR) of Beckmann fading, which permits to circumvent the inherent analytical complexity of this model. This is a new and useful result, as it is key for evaluating several important performance metrics of different wireless communication systems and also permits to readily compute the moments of the output SNR. Thus, we obtain simple exact expressions for the energy detection performance in Beckmann fading channels, both in terms of the receiver operating characteristic (ROC) curve and of the area under ROC curve. We also analyze the outage probability in interference limited systems affected by Beckmann fading, as well as the outage probability of secrecy capacity in wiretap Beckmann fading channels. Monte Carlo simulations have been performed to validate the derived expressions.

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Section: Area Under the Roc Curvementioning

confidence: 99%

Generalized MGF of Beckmann Fading With Applications to Wireless Communications Performance Analysis

Peña-Martín

Romero-Jerez

López‐Martínez

2017

IEEE Trans. Commun.

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“…Note that, in both ROC and AUC, the values of P fa and P d are calculated by (7) and (8), in the absence of noise uncertainty, and by (10) and (11), in the otherwise case, considering the α-η-κ-µ fading channel. Several authors have evaluated the performance, numerically or analytically, of the spectral sensing of the ED technique in terms of the AUC, under several types of fading channels [2], [24], [30], [37], [39].…”

Section: Noise Power Uncertaintymentioning

confidence: 99%

Analysis of Energy Detection with Noise Uncertainty over alpha-eta-kappa-mu Fading Channel

Rennó¹,

Souza²,

Yacoub

2019

JCIS

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One of the main challenges in implementing spectrum sensing techniques concerns the system robustness against noise power uncertainties. In this paper, a performance analysis of energy detection-based cognitive radio system is carried out assuming absence/presence of noise uncertainty for the sensing channel modeled by the α-η-κ-µ fading channel. The detection capacity of spectral sensing over several representative scenarios is analyzed with the theoretical results validated through Monte Carlo simulations. The results show that the performance of the spectral sensing technique is drastically affected by the noise uncertainty as well as by the channel conditions described the fading parameters α, η, κ, and µ. Both the ratio of the power of the dominant component to the power of the scattered and quadrature scattering signal and the clustering imbalance have a smaller impact on the overall performance. The offered results are especially useful in assessing the effect of fading in energy detector-based cognitive radio communication systems. Index Terms-Spectrum sensing, energy detector, α-η-κ-µ fading model, noise uncertainty. I. INTRODUCTION W ITH the exponential increase in demand for mobile communications services, the electromagnetic spectrum of radio frequencies tends to become increasingly congested. Regulatory agencies have used fixed band allocation policy, where the resource is allocated by type of service and users acquire the right to exploit certain bandwidth. This management policy results in underutilization of the resource, since the holder of the right to use a particular band may not do it all time and in all area where the service is offered. Consequently, there is a contradictory scenario in which the resource is at the same time scarce and underutilized, generating a great challenge for future mobile technologies, for example 5G and beyond. In this context, the concept of cognitive radio (CR) [2] arises, which proposes, among other functionalities, the opportunistic use of the spectrum. To perform this task, CR carries out the spectral sensing [2], which consists of

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“…Square Law Combining (SLC): Similar to MRC, the SLC scheme requires the received signals are integrated and squared, further the output signals are summed together [51]. The decision model follows a central chi-square distribution with 2Lu degrees of freedom if the binary hypothesis testing is H 0 , or a non-central chi-square distribution with 2Lu degrees of freedom under binary hypothesis testing H 1 .…”

Section: Cooperative Spectrum Sensing With Diversity Reception Slc An...mentioning

confidence: 99%

Cooperative spectrum sensing over generalized fading channels based on energy detection

Huang

Yuan

2018

China Commun.

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This paper analyzes the unified performance of energy detection (ED) of spectrum sensing (SS) over generalized fading channels in cognitive radio (CR) networks. The detective performance of SS schemes will be obviously affected by fading channel among communication nodes, and ED has the advantages of fast implementation, no requirement of priori received information and low complexity, so it is meaningful to investigate ED that is performed over fading channels such as Nakagami-m channel and Rice channel, or generalized fading channels such as κ-μ fading distribution and η-μ fading distribution. The α-κ-μ fading distribution is a generalized fading model that represents the nonlinear and small-scale variation of fading channels. The probability density function (p.d.f.) of instantaneous signal-to-ratio (SNR) of α-κ-μ distribution is derived from the p.d.f. of envelope to evaluate energy efficiency for sensing systems. Next, the detection probability of detective model with Marcum-Q function has been derived and the close-form expressions with moment generating function (MGF) method are deduced to achieve SS. Furthermore, novel and exact closed-form analytic expressions for average area under the receiver operating characteristics curve ( AUC ) also have been deduced to analyze the performance characteristics of ED over α-κ-μ fading channels. Besides, cooperative spectrum sensing (CSS) with diversity reception has been applied to improve the detection accuracy and mitigate the shadowed fading features with OR-rule. At last, the results show that the detection capacity can be evidently affected by α-κ-μ fading conditions, but appropriate channel parameters will improve sensing performance. On the other hand, the established ED-fading pattern is approved by simulations under diversity combination with CSS at the receiving end and it can significantly enhance the detection performance of proposed algorithms.

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Area under ROC curve of energy detection over generalized fading channels

Cited by 8 publications

References 40 publications

Generalized MGF of Beckmann Fading With Applications to Wireless Communications Performance Analysis

Generalized MGF of Beckmann Fading With Applications to Wireless Communications Performance Analysis

Analysis of Energy Detection with Noise Uncertainty over alpha-eta-kappa-mu Fading Channel

Cooperative spectrum sensing over generalized fading channels based on energy detection

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