Energy detector's performance analysis over the wireless channels with composite multipath fading and shadowing effects using the AUC approach

Alam, Shumon; Annamalai, A.

doi:10.1109/ccnc.2012.6181162

Cited by 10 publications

(21 citation statements)

References 22 publications

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“…The number of high-order derivatives of the MGF is determined by the timebandwidth product of energy detection. In [11], the average…”

Section: Introductionmentioning

confidence: 99%

“…The number of high-order derivatives of the MGF is determined by the timebandwidth product of energy detection. In [11], the average AUC is expressed by an infinite series and a high-order derivative of the MGF of the received SNR needs to be calculated for each term. Clearly, the computational complexity of the AUC analysis in [10] and [11] can become large, depending on the number of terms required and the complexity of the associated MGF when high-order derivatives are calculated.…”

Section: Introductionmentioning

confidence: 99%

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A unified MGF based approach to the analysis of area under the ROC curve of energy detection over generalized fading channels

Shi

2013

2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013)

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The performance of energy detection, in terms of the area under receiver operating characteristic (AUC), is investigated. By using its contour integral representation, the generalized Marcum Q-function is reformulated as an analytically attractive exponential integral, which in turn allows for a unified moment generating function (MGF) based approach to the AUC analysis of energy detection in an arbitrarily fading channel. The proposed MGF approach is then modified to include square-law combining (SLC) diversity and is further extended to address partial AUC with SLC.Index Terms-Generalized Marcum Q-function, moment generating function, energy detection I. INTRODUCTIONAs a classic application of noncoherent detection, energy detection [1] has attracted renewed interest recently, due primarily to its application in spectrum sensing for cognitive radio and dynamic spectrum access. Usually, the performance of energy detection is characterized by its probability of detection and probability of false alarm, and thereby fully described by the receiver operating characteristic (ROC) curve [2]-[7]. It is, however, inconvenient sometimes to compare different schemes using their ROC curves that may cross and hence cannot provide a simple definitive conclusion. In view of this situation, the area under the receiver operating characteristic (AUC) has been proposed in [8] as a single figure of merit to measure the performance of energy detection. The main difference between ROC and AUC is that ROC depends on the decision threshold whereas the effect of the threshold on AUC has been averaged out.Performance analysis of energy detection in terms of AUC has received relatively little attention so far. In [9], the moment generating function (MGF) of the received SNR, with the help of contour integration, is used to derive exact closedform expressions for the average AUC of energy detection in Nakagami-m fading with integer m and in η − µ fading with integer µ. But it is difficult to extend this approach to more general cases because the residual calculation involved can be challenging. In [10], the average AUC is given in an exact closed-form expression, for which high-order derivatives of the MGF of the received SNR are essential. The number of high-order derivatives of the MGF is determined by the timebandwidth product of energy detection. In [11], the average

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“…The number of high-order derivatives of the MGF is determined by the timebandwidth product of energy detection. In [11], the average…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A unified MGF based approach to the analysis of area under the ROC curve of energy detection over generalized fading channels

Shi

2013

2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013)

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“…As such, a Rayleigh-lognormal composite channel can be modeled by a K distribution, and a Nakagami-lognormal composite channel can be modeled by a generalized K distribution. This approximate technique has been used in [8]- [12]. In [13], the Wald distribution (also known as inverse Gaussian distribution) is used to approximate the lognormal distribution.…”

Section: Introductionmentioning

confidence: 99%

On the performance of energy detection for spectrum sensing in cognitive radio over Nakagami-lognormal composite channels

Shi

2013

2013 IEEE China Summit and International Conference on Signal and Information Processing

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This paper is concerned with the performance of energy detection for spectrum sensing in cognitive radio. Both fading and shadowing are considered by assuming a Nakagamilognormal composite channel model. Based on a newly-derived exponential integral representation for the generalized Marcum Q-function, performance analysis of energy detection is formulated as a unified moment generating function (MGF) approach. Energy detection without diversity is addressed first. The proposed MGF approach is then extended to the case of energy detection employing square-law combining (SLC) and squarelaw selection (SLS). With the help of an accurate approximation to the MGF of the Nakagami-lognormal composite channel, the effects of fading and shadowing on the performance of energy detection are investigated numerically.

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“…9,10 The role of LN in capturing the statistics of the composite fade of two or more simultaneous fading mechanism has been studied extensively in the previous. 17,18 Researchers have proposed a simple alternative structure to model shadowing effects known as the gamma distribution. 16 Therefore, the use of LN is not much entertained in the literature for the composite statistics.…”

Section: Introductionmentioning

confidence: 99%

“…[11][12][13][14][15] However, the complex mathematical representation of LN distribution endeavours analytical intractability in deriving the closed-form solutions of various digital communication performance metrics, [1][2][3][4][5][6][7][8] and it complicates the analysis of composite fading. [17][18][19] It facilitates obtaining the closed-form structure of various composite models and their performance analysis. 17,18 Researchers have proposed a simple alternative structure to model shadowing effects known as the gamma distribution.…”

Section: Introductionmentioning

confidence: 99%

Error probability and channel capacity analysis of wireless system over inverse gamma shadowed fading channel with selection diversity

Pant

Chauhan

Soni

2019

Int J Communication

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In this paper, we present an analytical framework to analyse the error probability and the channel capacity of the inverse gamma (I-Gamma) shadowed fading channel. First, the work discusses the utility of the I-Gamma over log-normal (LN) and gamma fading models where the closeness of I-Gamma with other existing shadowing models is carried out. Utilising the probability density function (PDF) of the I-Gamma shadowed fading channel, various metrics of the communication system, namely, the average symbol error probability (SEP), the channel capacity under optimal rate adaptation (ORA), channel inversion with fixed rate (CIFR), and truncated CIFR (TIFR) are derived. Further, the work is extended to derive a novel selection combining (SC) PDF, and the analytical results for the SEP and the channel capacity of SC diversity are presented. Furthermore, we also provide simpler asymptotic expressions for the average SEP. In addition, the simplified high and low signal-to-noise-ratio (SNR) solutions to channel capacity are also provided. The derived mathematical formulations have been endorsed by comparing with Monte Carlo simulations. KEYWORDSBessel K, bit error rate, capacity, distribution, fading Int J Commun Syst. 2019;32:e4083.wileyonlinelibrary.com/journal/dac

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Energy detector's performance analysis over the wireless channels with composite multipath fading and shadowing effects using the AUC approach

Cited by 10 publications

References 22 publications

A unified MGF based approach to the analysis of area under the ROC curve of energy detection over generalized fading channels

A unified MGF based approach to the analysis of area under the ROC curve of energy detection over generalized fading channels

On the performance of energy detection for spectrum sensing in cognitive radio over Nakagami-lognormal composite channels

Error probability and channel capacity analysis of wireless system over inverse gamma shadowed fading channel with selection diversity

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