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Optimal resource allocation in underlay cognitive radio networks based on partial CSI

机译:基于部分CSI的底层认知无线电网络中的最佳资源分配

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In this work, we consider an underlay cognitive radio networks where the secondary transmitter transmits signal with power based on sensing result of the signal power transmitted by the primary transmitter. To improve the signal quality of the secondary transmission, we proposed an recursive algorithm to alternatively optimize multi-level power allocation and quantization regions of the sensing result subject to satisfy the constraint on the outage probability of the primary transmission. It shows through simulation results that the multi-level power allocation achieves high rate of the secondary transmission when the SNR of the primary transmission is low. On the other hands, with high SNR of primary transmission, the multi-level power allocation will converges to two-level power allocation. It implies that if we can obtain reliable sensing result during the first slot, the multi-level power allocation strategy can be simplified to a binary power allocation.
机译:在这项工作中,我们考虑了一个底层认知无线电网络,其中次级发射机根据初级发射机发射的信号功率的感应结果来发射具有功率的信号。为了提高二次传输的信号质量,我们提出了一种递归算法来交替优化感测结果的多级功率分配和量化区域,以满足对一次传输中断概率的约束。通过仿真结果表明,当一次传输的信噪比较低时,多级功率分配可以实现二次传输的高速率。另一方面,在一次传输的SNR高的情况下,多级功率分配将收敛为两级功率分配。这意味着如果我们能够在第一个时隙期间获得可靠的感测结果,则可以将多级功率分配策略简化为二进制功率分配。

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