Spectrum sensing for cognitive radio with selection combining receiver antenna diversity

Namdar, Mustafa; İlhan, Hacı; Durak–Ata, Lütfiye

doi:10.1109/siu.2013.6531289

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…The cooperation method is based on DF relaying in which the source information is decoded first and then retransmitted to the destination. We consider that the received signals from the source and the relays are combined together using the method of equal gain combining (EGC) [15,17,28]. We assume that the diversity paths between the source-to-relay terminals (S → R j ) and the relay terminals-to-destination (R j → D) are independent non-identically distributed Nakagami-m fading.…”

Section: Mobile Radio Communications Systems Using Cooperative-diversmentioning

confidence: 99%

Performance Analysis of the Differential Evolution and Particle Swarm Optimization Algorithms in Cooperative Wireless Communications

Başgümüş¹,

Namdar²,

Yılmaz³

et al. 2016

Optimization Algorithms - Methods and Applications

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In this study, we evaluate the performance of differential evolution (DE) and particle swarm optimization (PSO) algorithms in free-space optical (FSO) and mobile radio communications systems. In particular, we obtain the optimal transmission distances for multiple-relay nodes in FSO communication systems and optimal relay locations in mobile radio communications systems for the cooperative-diversity networks, using both algorithms. We investigate the performance comparison of DE and PSO algorithms for the parallel decode-and-forward (DF) relaying. Then, we analyze the cost functions. Furthermore, we present the execution time and the stability of the DE and PSO algorithms.

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Section: Mobile Radio Communications Systems Using Cooperative-diversmentioning

confidence: 99%

Performance Analysis of the Differential Evolution and Particle Swarm Optimization Algorithms in Cooperative Wireless Communications

Başgümüş¹,

Namdar²,

Yılmaz³

et al. 2016

Optimization Algorithms - Methods and Applications

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show abstract

“…Spectrum utilization can be increased by opportunistically allowing the unlicensed secondary user (SU) to utilize a licensed band in the absence of the primary user (PU) [1][2][3][4]. The ability of providing awareness about the usage of the frequency spectrum or the detection of the PU in a desired frequency band lets the SU access the radio communication channel without causing harmful interference to the PU [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Outage Performance Analysis of Underlay Cognitive Radio Networks with Decode‐and‐Forward Relaying

Namdar¹,

Başgümüş²

2017

Cognitive Radio

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In this chapter, we evaluate the outage performance of decode-and-forward relaying in cognitive radio networks over Rayleigh fading channels, subject to the relay location for a secondary user. In particular, we obtain the optimal relay location in wireless communications systems for the cognitive radio networks, using differential evolution optimization algorithm. Then, we investigate the optimal transmission rate of the secondary user. We present the numerical results to validate the proposed theoretical analysis and to show the effects of the Rayleigh fading channel parameters for the whole system performance.

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Selective sampling and otsu algorithm based spectrum sensing in cognitive radio networks

Namdar

Koçak

Başgümüş

2017

2017 25th Signal Processing and Communications Applications Conference (SIU)

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Spectrum sensing for cognitive radio with selection combining receiver antenna diversity

Cited by 3 publications

References 10 publications

Performance Analysis of the Differential Evolution and Particle Swarm Optimization Algorithms in Cooperative Wireless Communications

Performance Analysis of the Differential Evolution and Particle Swarm Optimization Algorithms in Cooperative Wireless Communications

Outage Performance Analysis of Underlay Cognitive Radio Networks with Decode‐and‐Forward Relaying

Selective sampling and otsu algorithm based spectrum sensing in cognitive radio networks

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