A Novel Genetic-Based Resource Allocation and Cooperative Node Selection Technique for Physical Layer Security Designs

Okati, Niloofar; Mosavi, Mohammad Reza; Behroozi, Hamid

doi:10.1007/s11277-017-4116-4

Cited by 2 publications

(2 citation statements)

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“…To the best of our knowledge, very little works have been done in CCRNs by using a GA. Okati et al [ 27 ] studied a novel GA which can solve the resource allocation (RA) and cooperative node selection problems for maximizing the secrecy capacity of wireless communications networks. The RA and RS problems are jointly studied by Fang et al [ 28 ] for maximizing the capacity of a cooperative wireless networks.…”

Section: Related Workmentioning

confidence: 99%

“…To solve the optimization problem in Equation ( 20 ), in the paper we adopt a general GA which includes the initialization of the population, evaluation of fitness function, and some genetic operations, such as the selection, crossover, and mutation [ 27 , 28 , 29 ]. The GA also requires a repeated iteration process until a near-optimal solution is obtained by the algorithm [ 30 , 31 ].…”

Section: The Proposed Ga-based Mrs and Pa Scheme For Ccrnsmentioning

confidence: 99%

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Joint Relay Selection and Power Allocation through a Genetic Algorithm for Secure Cooperative Cognitive Radio Networks

Rahman

Lee

Koo

2018

Sensors

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In cooperative cognitive radio networks (CCRNs), there has been growing demand of transmitting secondary user (SU) source information secretly to the corresponding SU destination with the aid of cooperative SU relays. Efficient power allocation (PA) among SU relays and multi-relay selection (MRS) are a critical problem for operating such networks whereas the interference to the primary user receiver is being kept below a tolerable level and the transmission power requirements of the secondary users are being satisfied. Subsequently, in the paper, we develop the problem to solve the optimal solution for PA and MRS in a collaborative amplify-and-forward-based CCRNs, in terms of maximizing the secrecy rate (SR) of the networks. It is found that the problem is a mixed integer programming problem and difficult to be solved. To cope with this difficulty, we propose a meta-heuristic genetic algorithm-based MRS and PA scheme to maximize the SR of the networks while satisfying transmission power and the interference requirements of the networks. Our simulation results reveal that the proposed scheme achieves near-optimal SR performance, compared to the exhaustive search scheme, and provides a significant SR improvement when compared with some conventional relay selection schemes with equal power allocation.

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Section: Related Workmentioning

confidence: 99%