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Secure Robust Ergodic Uplink Resource Allocation in Relay-Assisted Cognitive Radio Networks

机译:中继辅助认知无线电网络中安全稳健的遍历上行资源分配

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We investigate the ergodic uplink resource allocation problem for secure communication in relay-assisted orthogonal frequency-division multiple access (OFDMA)-based cognitive radio networks (CRNs) in the presence of a set of passive eavesdroppers where relay nodes assist the legitimate users to transmit their messages. Previous works have commonly assumed the availability of channel state information (CSI) for this type of problems. However, due to the decentralized nature of CRNs and hidden activities of eavesdroppers, the assumption of availability of exact values of CSI is not realistic. In this paper, we consider uncertainty on the estimated values of CSI between different transmitters and receivers, e.g., CSI between each legitimate transmitter and its corresponding receiver and CSI of each legitimate user and each eavesdropper. We utilize the worst-case robust formulation to find power and sub-carrier allocations in such a way that under the worst condition of error, the regulatory constraints imposed to CRN are satisfied and the secrecy rate of each secondary legitimate user is stabilized. It is well known that the robust approaches impose a high computational complexity to the system and reduce the system performance as they conservatively consider the error in the maximum extent. We demonstrate how the robust formulation can be significantly simplified and tradeoff parameters can be introduced to moderate the effect of the worst-case approach. Simulation results are provided to demonstrate the performance of CRNs for different uncertain system parameters.
机译:我们研究了遍历上行链路资源分配问题,用于在存在一组无源窃听者(其中中继节点协助合法用户进行传输)的情况下,在基于中继辅助的正交频分多址(OFDMA)的认知无线电网络(CRN)中进行安全通信的问题他们的信息。以前的工作通常假定此类问题的信道状态信息(CSI)可用。但是,由于CRN的分散性质和窃听者的隐藏活动,因此无法获得CSI准确值的假设。在本文中,我们考虑了不同发射机和接收机之间CSI估计值的不确定性,例如,每个合法发射机与其对应的接收机之间的CSI以及每个合法用户和每个窃听者的CSI。我们采用最坏情况的鲁棒公式来查找功率和子载波分配,以便在最坏的错误条件下,满足CRN的监管约束,并稳定每个次要合法用户的保密率。众所周知,健壮的方法给系统带来了很高的计算复杂度,并降低了系统性能,因为它们保守地最大程度地考虑了误差。我们演示了如何可以显着简化健壮公式,并可以引入折衷参数来缓和最坏情况方法的影响。仿真结果提供了证明CRNs在不同不确定系统参数下的性能。

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