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

机译:安全协同认知无线电网络的遗传算法联合中继选择和功率分配

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摘要

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.
机译:在协作式认知无线电网络(CCRN)中,对借助协作式SU中继秘密地将次要用户(SU)源信息秘密传输到相应的SU目的地的需求不断增长。 SU中继之间的有效功率分配(PA)和多中继选择(MRS)是操作此类网络的关键问题,而对主要用户接收器的干扰保持在可容忍的水平以下,并且次要用户的传输功率要求感到满意。随后,在本文中,我们从最大化网络的保密率(SR)的角度出发,提出了在基于协作放大和转发的CCRN中解决PA和MRS最优解决方案的问题。发现该问题是混合整数规划问题,难以解决。为了解决这一难题,我们提出了一种基于元启发式遗传算法的MRS和PA方案,以在满足传输功率和网络干扰要求的同时,最大化网络的SR。我们的仿真结果表明,与穷举搜索方案相比,该方案可实现近乎最佳的SR性能,并且与某些具有相等功率分配的常规中继选择方案相比,可显着改善SR。

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