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Robust beamforming and power allocation in cognitive radio relay networks with imperfect channel state information

机译:具有不完善信道状态信息的认知无线电中继网络中的稳健波束成形和功率分配

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The authors study robust beamforming and power allocation in cognitive relay networks using several relays in the secondary link. The channel state information is imperfect modelled by Gaussian random variables. First, minimisation of the total transmit power of relays and the secondary transmitter is considered with a constraint on the interference in the primary receiver. Also, a constraint is assumed on the received signal-to-interference plus noise ratio (SINR) in the secondary receiver. In the other scenario, maximisation of the SINR in the secondary receiver is investigated with a limit on the maximum transmit power of relays and the secondary transmitter. For each non-convex optimisation problem, an iterative and robust algorithm is derived. It is proved that the first proposed algorithm converges to the global optimum. Simulation results similarly show the convergence of both algorithms with a few number of iterations. The outperformance of the robust algorithms is shown compared with a non-robust design.
机译:作者研究了使用辅助链路中的多个中继的认知中继网络中的鲁棒波束成形和功率分配。信道状态信息不是由高斯随机变量完美建模的。首先,考虑到中继器和辅助发射器的总发射功率的最小化,并限制了主要接收器中的干扰。此外,假设在辅助接收机中对接收到的信号干扰加噪声比(SINR)施加约束。在另一种情况下,将在中继器和辅助发射机的最大发射功率受到限制的情况下研究辅助接收机中SINR的最大化。对于每个非凸优化问题,得出了一种迭代且鲁棒的算法。证明了所提出的第一个算法收敛于全局最优。仿真结果类似地显示了两种算法经过几次迭代的收敛性。与非鲁棒设计相比,显示了鲁棒算法的出色性能。

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