School of Electrical Engineering, University of Ulsan, 93-Daehak-ro, Namgu, Ulsan 44610, Korea.
Sensors (Basel). 2018 Nov 14;18(11):3934. doi: 10.3390/s18113934.
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.
在协作认知无线电网络(CCRNs)中,越来越需要借助协作辅助用户(SU)中继来秘密传输辅助用户源信息到相应的辅助用户目的地。在这种网络中,SU 中继之间的有效功率分配(PA)和多中继选择(MRS)是一个关键问题,同时要将对主用户接收器的干扰保持在可容忍的水平,并满足辅助用户的传输功率要求。随后,在本文中,我们针对协作放大转发型 CCRNs 中的 PA 和 MRS 问题进行了研究,以最大化网络的保密速率(SR)。结果表明,该问题是一个混合整数规划问题,难以求解。为了解决这个困难,我们提出了一种基于遗传算法的启发式 MRS 和 PA 方案,以在满足网络传输功率和干扰要求的情况下最大化网络的 SR。我们的仿真结果表明,与穷举搜索方案相比,所提出的方案实现了接近最优的 SR 性能,并与一些具有等功率分配的传统中继选择方案相比,提供了显著的 SR 改善。