Outage probability of amplify-and-forward underlay cognitive relay networks with selection diversity over Nakagami-m fading channels

Alhulayil, Moawiah; Al-Mistarihi, Mamoun F.; Shurman, Mohammad

doi:10.1109/mipro.2015.7160327

Cited by 3 publications

(6 citation statements)

References 19 publications

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“…In the first phase, SS broadcasts the signal to SRs under the transmit power which guarantees that the interference on the PU receiver does not exceed the maximum tolerable interference power Q . As a result, the transmit power of SS is given by P s = Q /| h 1 | 2 [10–12, 17, 21], where h 1 represents the channel coefficient for the SS−PU link. Note that the single power constraint of the interference on the primary network is considered in our scenario as described in [10–12, 17, 21].…”

Section: System Modelmentioning

confidence: 99%

“…In the second phase, the selected relay k amplifies the received signal with a variant gain and forwards it to the destination. The transmit power of SR i [10–12, 17, 21] is P i = Q /| h 2, i | 2 , where h 2, i denotes the channel coefficient for the SR i −PU link. Let f i and g i be the channel coefficients of the SS−SR i and the SR i −SD links, respectively.…”

Section: System Modelmentioning

confidence: 99%

“…The exact OP is expressed as

P_{out} \equiv Pr false[1 / 2 \cdot {log}_{2} false(1 + γ_{k} false) < normalℵ false] = Pr false[γ_{k} < z false],

where

z = 2^{2 normalℵ} - 1

. Using the fact that

γ_{k} \leq \min false(γ_{f_{k}}, γ_{g_{k}} false)

in [4–6, 12, 17], the lower bound of the OP is

P_{out}^{LB} = Pr false[\min false(γ_{f_{k}}, γ_{g_{k}} false) < z false] .

…”

Section: Outage Performance Analysismentioning

confidence: 99%

“…For Nakagami‐ m fading channels, Xing et al [16] studied the performance of a cognitive DF relay network with imperfect CSI of the interference links. In [17, 18], the OP performance of cognitive AF relay network was evaluated. Zhang et al [19] analysed the performance of an underlay cognitive DF relay network with the N th BRS over Nakagami‐ m fading channels.…”

Section: Introductionmentioning

confidence: 99%

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Performance of switching‐based partial relay selection scheme for amplify‐and‐forward cognitive relay networks

2016

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

“…The exact OP is expressed as

P_{out} \equiv Pr false[1 / 2 \cdot {log}_{2} false(1 + γ_{k} false) < normalℵ false] = Pr false[γ_{k} < z false],

where

z = 2^{2 normalℵ} - 1

. Using the fact that

γ_{k} \leq \min false(γ_{f_{k}}, γ_{g_{k}} false)

in [4–6, 12, 17], the lower bound of the OP is

P_{out}^{LB} = Pr false[\min false(γ_{f_{k}}, γ_{g_{k}} false) < z false] .

…”

Section: Outage Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Performance of switching‐based partial relay selection scheme for amplify‐and‐forward cognitive relay networks

2016

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“…In addition, the communication between the source and destination of the SU network was not available through a direct link due to sever shadowing. On the other hand, an enhanced CRN model with a direct link was considered in [55]. An SC scheme was deployed at the destination.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Dual-Hop AF Cognitive Relay Networks with Best Selection and Interference Constraints

2022

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In this paper, a dual-hop underlay cognitive relay network (CRN) with a best-relay selection (BRS) scheme under spectrum-sharing constraints from the primary user (PU) is investigated. The system model in this work consists of one PU, one secondary user (SU) and a few SU relays. Both users exchange the information using a half-duplex mode through amplify-and-forward (AF) SU relays. Moreover, all channels are modelled using the Nakagami-m fading model. In this work, the outage probabilities (OPs) are derived for the proposed system model under several scenarios to investigate the network performance under interference power constraint Ip at the PU receiver. In addition, the impacts of the number of relays and the channel fading severity parameters are investigated as well. Furthermore, the system performance is investigated for several PU locations. The various numerical results are verified using a Monte Carlo simulation. Overall, the obtained results show that several factors such as the number of relays, channel fading severity parameters and PU location have a major impact on the outage performance of the SU. The simulation and analytical results are perfectly matched, confirming the accuracy of the analytical derivations.

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Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User

et al. 2023

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In this work, a cognitive relay network (CRN) with interference constraint from the primary user (PU) with a mobile end user is studied. The proposed system model employs a half-duplex transmission between a single PU and a single secondary user (SU). In addition, an amplify and forward (AF) relaying technique is employed between the SU source and SU destination. In this context, the mobile end user (SU destination) is assumed to move at high vehicular speeds, whereas other nodes (SU Source, SU relays and PU) are assumed to be stationary. The proposed scheme dynamically determines the best relay for transmission based on the highest signal-to-noise (SNR) ratio by deploying selection combiner at the SU destination, thereby achieving diversity. All channels connected with the stationary nodes are modelled using Rayleigh distribution, whereas all other links connected with the mobile end user are modelled using Nakagami-m fading distribution (m<1). The outage probabilities (OPs) are obtained considering several scenarios and Monte Carlo simulation is used to verify the numerical results. The obtained results show that a variety of factors, including the number of SU relays, the severity of the fading channels, the position of the PU, the fading model, and the mobile end user speed, might influence the CRN’s performance.

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Outage probability of amplify-and-forward underlay cognitive relay networks with selection diversity over Nakagami-m fading channels

Cited by 3 publications

References 19 publications

Performance of switching‐based partial relay selection scheme for amplify‐and‐forward cognitive relay networks

Performance of switching‐based partial relay selection scheme for amplify‐and‐forward cognitive relay networks

Performance Analysis of Dual-Hop AF Cognitive Relay Networks with Best Selection and Interference Constraints

Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User

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