Adaptive L_p—Norm Spectrum Sensing for Cognitive Radio Networks

Abstract-We study the problem of determining the optimum power allocation policy for an average power constrained jammer operating over an arbitrary additive noise channel, where the aim is to minimize the detection probability of an instantaneously and fully adaptive receiver employing the Neyman-Pearson (NP) criterion. We show that the optimum jamming performance can be achieved via power randomization between at most two different power levels. We also provide sufficient conditions for the improvability and nonimprovability of the jamming performance via power randomization in comparison to a fixed power jamming scheme. Numerical examples are presented to illustrate theoretical results.

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“…Based on (1), (12), and (13), the probability of false alarm expression can be obtained and threshold η γ can be calculated as follows:…”

Section: Numerical Results and Conclusionmentioning

confidence: 99%

“…Detection probability vs. average power constraint level β for optimal power randomization, on-off jammer, and no randomization approaches. and unit variance random variable, but it has heavier tails than a Gaussian random variable [12]. In addition, s = 5 and α = 0.001 are used.…”

Section: Numerical Results and Conclusionmentioning

confidence: 99%

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

et al. 2012

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“…As discussed in the last section, when ST fails to sense the existence of PT, it is possible to interfere in the normal transmission among PUs. A common spectrum sensing probability, value can be used to evaluate the interference caused by SUs, which is the missed detection probability ( md ) that ST falsely detects PT absence when PT is actually present [14]. Usually md is the function of distance from PT to ST .…”

Section: Spectrum-sensing Influence On Interferencementioning

confidence: 99%

Interference of Cognitive Wireless Networks on Rayleigh and Rice Fading Channels

2014

International Journal of Distributed Sensor Networks

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We propose a mathematical interference model of cognitive wireless networks based on two channels suffering different fading. The primary channel between primary transmitter and primary receiver is under Rayleigh fading. The secondary interference channel between secondary transmitter and primary receiver is under Rice fading. By employing various factors, such as the spectrum sensing scheme and the spatial distribution of the secondary users, the proposed model gives an accurate expression of interference probability compared to the statistical models that just approximate the probability model of the interference or bounds models that aim at finding the bounds of the interference. The proposed probability density function (PDF), cumulative distribution function (CDF), and the mean and variance of interference can be extended to a number of applications including power control, spectrum sensing evaluation, secondary users density analysis, and setting interference toleration threshold. Moreover, the framework developed in this paper can be applied to the interference analysis for the nodes of cognitive networks distributed in irregular geographical shapes like finite or infinite zone. The simulation result verifies our theory and is in agreement with the expectation.

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“…PolarityCoincidence-Array (PCA)-based spectrum sensing is proposed in [15], a significant performance enhancement is achieved by the PCA detector, but the prior knowledge such as the variance of the noise and the PU signal cyclic frequency are also needed in the algorithm, which is difficult in practical system. The Lp-Norm Spectrum Sensing method for cognitive radio networks is presented in [16]. It does not require any prior knowledge about the nonGaussian noise, but the condition that the noise do not have second-order statistics and high-order statistics such as α-stable distribution are not taken into account, there are limitations when applied.…”

Section: Introductionmentioning

confidence: 99%

Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

Dou

Shi

Lin

et al. 2017

J Wireless Com Network

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Motivated by the practical and accurate demand of intelligent cognitive radio (CR) sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the non-Gaussian colored noise based on α stable process and the sensing method is improved fractional low-order moment (FLOM) detection algorithm with balance parameter. First, we establish the non-Gaussian colored noise model through combining α-distribution with a linear system represented by a matrix. And a fitting curve of practical noise data is given to verify the validity of the proposed model. Then we present a parameter estimation method with low complexity to obtain the balance parameter, which is an important part of the detection algorithm. The balance parameter-based FLOM (BP-FLOM) detector does not require any a priori knowledge about the primary user signal and channels. Monte Carlo simulations clearly demonstrate the performance of the proposed method versus the generalized signal-to-noise ratio, the characteristic exponent α, and the number of detectors in sensing networks.

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Adaptive L_p—Norm Spectrum Sensing for Cognitive Radio Networks

Cited by 64 publications

References 25 publications

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

Optimum Power Allocation for Average Power Constrained Jammers in the Presence of Non-Gaussian Noise

Interference of Cognitive Wireless Networks on Rayleigh and Rice Fading Channels

Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

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