Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

Lei, Hongjiang; Zhang, Jianming; Park, Kihong; Ansari, Imran Shafique; Pan, Gaofeng; Alouini, Mohamed‐Slim

doi:10.1049/iet-com.2017.0131

Cited by 16 publications

(19 citation statements)

References 29 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It's the most direct application of probability theory in engineering. 3 Noise is a classic random process, and it is very important to minimize the probability of a bit or message being received in error due to that noise. 4 If the noise is Gaussian, as it usually is, and the modem is using a "coherent" binary scheme without error correction, then the probability of a given bit being in error is simply the area under some portion of the normal (Gaussian or "bell") curve that depends on the signal-to-noise ratio (SNR).…”

Section: Probability Density Functions In Engineering Applicationsmentioning

confidence: 99%

Generalized probability density function and applications to the experimental data in electrical circuits and systems

Özyapıcı

Bilgehan²,

Sensoy

2020

Circuit Theory & Apps

View full text Add to dashboard Cite

Summary The mathematical sciences, and particularly probabilistic and statistical methods, are key to understanding the dependencies of the systems. The purpose of this paper is to encourage a wider recognition by engineers of a new generalized principle which in its mathematical form is a powerful instrument for the solution of practical problems. Generalized probability density function was introduced to permit analysis without pre‐knowledge of the source of the data. The fundamental principles are extended to apply the most related engineering applications without the need to know the type of source generating the data. The generalized model presented eliminates preliminary work in engineering problems. The proposed model introduces an exponential density function to produce a direct solution to randomly varying data. The exponential density function is fully compatible with applications containing randomly distributed data. The success of the generalized model presented is due to the calculated parameters in the exponential density function. The method is applied to various problems chosen from the field of engineering with great success.

show abstract

Section: Probability Density Functions In Engineering Applicationsmentioning

confidence: 99%

Generalized probability density function and applications to the experimental data in electrical circuits and systems

Özyapıcı

Bilgehan²,

Sensoy

2020

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…It is assumed that all the channels within the network are subjected to Rayleigh fading distribution with the average channel power gains of Ω 1 and Ω 2 for MU and PU, respectively. The channel fading with estimation error between the AP‐to‐MU can be defined as 4,25

{\overset{false~}{h}}_{1} = \sqrt{ρ} h_{1} + \sqrt{1 - ρ} ψ,

where h 1 is the channel gain between the AP and the MU, ψ is the estimation error having the same complex Gaussian distribution as h 1 , and ρ denotes the power correlation coefficient with value 0 ≤ ρ ≤ 1. However, due to the noise and the signal distortion or delay of channel estimation, it is difficult to obtain the ideal CSI from MU in most of practical scenes.…”

Section: System Modelmentioning

confidence: 99%

“…When SC is employed at the MU, the combiner power gain can be defined as

X_{SC} = \max_{k \in ({}, 1, 2 \dots N)} {|{\overset{false~}{h}}_{1}|}^{2}

. The PDF for the scheme can thus be expressed as 4,28

f_{SC} (x) = {falsefalse}_{\sum}^{k = 0} \frac{ξ (k)}{Ω_{1}} \exp (- \frac{normalx normalμ}{Ω_{1}}),

where

({, \begin{matrix} normalξ ((), k) = \frac{N {(- 1)}^{k} ((), \binom{N - 1}{k})}{((), 1 - ρ) ϕ} \\ ϕ = \frac{ρ}{1 - ρ} + k + 1 \\ μ = \frac{1}{1 - ρ} - \frac{ρ}{ϕ {(1 - ρ)}^{2}} \end{matrix}) .

…”

Section: Channel and Mobility Statistical Modelsmentioning

confidence: 99%

“…This concept of cognitive radio significantly improves the spectral efficiency of wireless systems by allowing the unlicensed secondary user(s) (SUs) to access the licence resource spectrum of the primary user(s) (PUs) 3 . This is highly possible through the three spectrum sharing strategies such as underlay, overlay and interweaves mode of operations 4 . In underlay mode, the SU is permitted to access the licence spectrum of the PU receiver simultaneously under the interference constraint provided that its transmission power is below certain threshold.…”

Section: Introductionmentioning

confidence: 99%

“…The researches have depicted that MRC is an optimal scheme and it outperforms the SC under the same system condition . Over the past few years, the existing studies on the performance of the cognitive radio system only considered static receiver or user 1–4,7–9 . However, user mobility is very critical in quantifying the characteristic of any wireless system due to the time varying nature of the received power.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the performance of underlay cognitive radio system with random mobility under imperfect channel state information

Odeyemi

Owolawi

Olakanmi

2020

Int J Communication

View full text Add to dashboard Cite

Summary In this paper, the performance of an underlay cognitive radio system with random mobility and imperfect channel state information (CSI) is investigated. The mobile user (MU) utilises maximum ratio combining (MRC) and selection combining (SC) diversity techniques as signal reception to improve the quality of received signal‐to‐noise ratio (SNR). Under the Rayleigh fading, random waypoint mobility model is employed to characterised the effect of the MU random mobility on the system performance. Thus, novel probability density function (PDF) and cumulative distribution function (CDF) for the two considered diversity techniques are derived. Through these, the outage probability and average bit error rate (ABER) closed‐form analytical expressions are then obtained to quantify the system performance under the MRC and SC schemes. The results illustrate the effect of imperfect CSI, user mobility which is characterised by pathloss and the network topology on the system performance. Also, the results depict that MRC offers the system better performance compared with SC under the same system conditions. The accuracy of the derived analytical expressions is verified through Monte‐Carlo simulations.

show abstract

Secrecy analysis of multiple‐input multiple‐output underlay cognitive radio networks with energy harvesting

Thakur

Kumar

Gupta

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The cognitive radio (CR) technique is an encouraging approach to deal with spectrum scarcity in wireless networks. However, this technique raises the issues of secrecy and privacy in the CR network. In this work, a multiple-input multiple-output underlay CR network is considered, which consists of a primary network, a secondary network, and an eavesdropper. All the primary, secondary as well aseavesdropper's nodes are assumed to have multiple antennas with radio frequency energy harvesters to enhance their spectral and energy efficiency properties. The eavesdropper node manipulates it's simultaneous wireless information and power transfer architecture in a pursuit to intercept the secure communication between the cognitive transmitter and the cognitive receiver. This technique and considered network are combined with the traditional selection combining (SC) and maximal ratio combining (MRC) scheme and the results are compared. The Monte Carlo simulation is done for the validation of results. The main aim is to drive the closed-form analytical expression for exact and asymptotic secrecy outage probability. The present results show that the SC scheme at eavesdropper provides better secrecy in comparison to the MRC technique and also the number of antennas affects the secrecy of the system. Moreover, the impact of power splitter is considered and also the optimal value of power splitter is derived to maximize information decoding.Recently essence of connected devices especially in smart cities has given rise to the generation of a large volume of data. However, the spectrum underutilization, scarcity of spectrum, and security are still major issues of concern. The cognitive radio network (CRN) is an encouraging explanation to alleviate the spectrum scarcity and underutilization issue as it increases the spectral as well as energy efficiency. 1 In CRN, the unlicensed users are also known as secondary users (SU) are authorized to access the spectrum without creating any conflict to the licensed users also known as the primary user (PU). 2 The CRN works under underlay, overlay, and interweave mode. 3 Underlay mode is one of the most important components of the CRN where SUs transmit along with the PU by adjusting the transmit power below a threshold point which can be tolerated by the PU. However, the limited battery life of conventional wireless networks is also becoming another bottleneck issue. The duration of a battery can be extended up to a certain limit by charging or replacing the battery but at the cost of increased price and environment degradation. One of the innocuous and greener solutions for energy harvesting (EH) is simultaneous wireless information and power transfer (SWIPT). 4,5 The SWIPT system utilizes Trans Emerging Tel Tech.

show abstract

Secrecy performance analysis of SIMO underlay cognitive radio systems with outdated CSI

Cited by 16 publications

References 29 publications

Generalized probability density function and applications to the experimental data in electrical circuits and systems

Generalized probability density function and applications to the experimental data in electrical circuits and systems

On the performance of underlay cognitive radio system with random mobility under imperfect channel state information

Secrecy analysis of multiple‐input multiple‐output underlay cognitive radio networks with energy harvesting

Contact Info

Product

Resources

About