2012
DOI: 10.1049/iet-com.2010.1037
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Statistical interference modelling and deployment issues for cognitive radio systems in shadow fading environments

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Cited by 7 publications
(7 citation statements)
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“…We assumed a pathloss only channel, in regards to the presented results in figure 3; we can see that the uncertainty of the location of the primary user increases interference in terms of mean and variance [33]. Also the log -normal approximation for the interference is prone to inaccuracy [34] as the interferences increases when compared to the power control scheme in figure 2. The following values for the parameters where used under the contention control scheme in figure 2; R= 100m, =3 user/10 4 m 2 , =4, =20m, =4, =1W, =0.5R.…”
Section: Resultsmentioning
confidence: 99%
“…We assumed a pathloss only channel, in regards to the presented results in figure 3; we can see that the uncertainty of the location of the primary user increases interference in terms of mean and variance [33]. Also the log -normal approximation for the interference is prone to inaccuracy [34] as the interferences increases when compared to the power control scheme in figure 2. The following values for the parameters where used under the contention control scheme in figure 2; R= 100m, =3 user/10 4 m 2 , =4, =20m, =4, =1W, =0.5R.…”
Section: Resultsmentioning
confidence: 99%
“…The novelty of our work is that it can suggest radius of exclusion zone and density of secondary users to achieve the maximum throughput in the HetNet with spectrum underlay, unlike the works cited above. The aim in works [8][9][10][11] is only to devise exclusion zones or permissible density of secondary users to offer protection to primary users. To the best of our knowledge, there is no effort to suggest exclusion zones and density of secondary users to achieve the maximum throughput in the context of an underlay spectrum sharing network.…”
Section: Pcmentioning
confidence: 99%
“…Some works have addressed the specifications of network deployment using mainly statistical models such as in [8] in which from a log-normal distribution, the authors find radius of exclusion zones and density of secondary users. In this context, in [9], an outage-based distributed user removal algorithm to devise the number of secondary users in a spectrum underlay HetNet is presented.…”
Section: Introductionmentioning
confidence: 99%
“…where μ 3 is the third central moment of |h(t)| [17]. The main contributions of this paper are as follows:…”
Section: Using Skewness In Channel Identificationmentioning
confidence: 99%
“…Skewness is a measure of symmetry, or more precisely, the lack of symmetry in the probability density function (PDF). A distribution, or data set, is symmetric if it looks the same to the left and right of the centre point; that is, if the bulk of the data is at the left and the right tail is longer, then the distribution is skewed right, or positively skewed; if the peak is towards the right and the left tail is longer, then it is said the distribution is skewed left, or negatively skewed [17]. Since skewness characterises the amount and direction of skew (departure from horizontal symmetry), it could be an effective parameter for LOS/NLOS classification; if the skewness of the impulse response of a UWB channel is high, then the channel is more likely to be LOS.…”
Section: Introductionmentioning
confidence: 99%