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Maximizing throughput for overlaid cognitive radio networks

机译:最大化认知无线网络的吞吐量

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We consider a cognitive radio network in which a set of base stations make opportunistic spectrum access to support wireless subscribers within their covering cells. The spectrum of interest is divided into independent channels licensed to the primary users. Channel assignment and power control must be carried out in the cognitive network so that no excessive interference is caused to the primary users in the overlaid network. We are interested in the downlink channel assignment and power control problem for a cognitive radio network, with the objective of maximizing the total throughput of all secondary users. We first develop a mathematical model and present a Mixed Integer Linear Programming formulation, which is generally NP-hard. Subsequently, to obtain a suboptimal control scheme with lower complexity, we develop a distributed optimization algorithm that iteratively increases the overall cognitive radio network throughput. Through simulation results, we compare the performance of the distributed optimization algorithm with the optimal and validate its efficacy.
机译:我们考虑一个认知无线电网络,其中一组基站进行机会频谱访问以支持其覆盖小区内的无线用户。感兴趣的频谱分为授权给主要用户的独立频道。信道分配和功率控制必须在认知网络中进行,以便不会对重叠网络中的主要用户造成过多干扰。我们对认知无线电网络的下行链路信道分配和功率控制问题感兴趣,目的是最大化所有次要用户的总吞吐量。我们首先开发一个数学模型,并提出一个混合整数线性规划公式,该公式通常是NP难的。随后,为了获得具有较低复杂度的次优控制方案,我们开发了一种分布式优化算法,该算法迭代地增加了整个认知无线电网络的吞吐量。通过仿真结果,我们比较了分布式优化算法与最优算法的性能,并验证了其有效性。

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