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Robust Resource Optimization for Cooperative Cognitive Radio Networks with Imperfect CSI

机译:具有不完善CSI的协作认知无线电网络的鲁棒资源优化

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We develop robust resource-allocation schemes for a cognitive radio network (CRN), where the secondary users (SUs) try to communicate with each other from different small cell primary user (PU) networks. User cooperation technique is considered for communication among the SUs since PUs are in close proximity and there are tight interference constraints on the PU bands. Power allocation and relay selection schemes are optimized with the provision of quality of service to each SU considering different channel uncertainty models. We incorporate the channel outage events that have resulted from the imperfect channel state information under slow-fading channels in our resource optimization algorithms. We maximize the system goodput of the CRN while satisfying the interference constraints of the PU bands both probabilistically and for the worst case scenario. The original probabilistic optimization problem is approximated and transformed into a convex deterministic form, and a closed-form analytical solution for power allocation is derived. The closed-form power allocation solution helps us to develop an efficient relay selection scheme based on Hungarian algorithm. Simulation results reveal the effectiveness of our proposed schemes and the implications of ignoring the imperfectness among different channels when developing resource-allocation algorithms for CRNs.
机译:我们为认知无线电网络(CRN)开发了健壮的资源分配方案,其中辅助用户(SU)尝试通过不同的小小区主要用户(PU)网络相互通信。由于PU距离很近并且在PU频段上存在严格的干扰约束,因此考虑了用户协作技术用于SU之间的通信。考虑到不同的信道不确定性模型,通过向每个SU提供服务质量来优化功率分配和中继选择方案。在资源优化算法中,我们将慢衰落信道下由于不完善的信道状态信息导致的信道中断事件纳入其中。我们在概率和最坏情况下都满足PU频带的干扰约束的同时,最大化了CRN的系统吞吐量。近似原始的概率优化问题并将其转化为凸确定性形式,并得出功率分配的封闭形式解析解。封闭式功率分配解决方案帮助我们开发基于匈牙利算法的有效继电器选择方案。仿真结果揭示了我们提出的方案的有效性,以及在开发CRN的资源分配算法时忽略了不同渠道之间不完美的含义。

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