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Resource allocation algorithm for downlink MIMO-OFDMA based cognitive radio networks in spectrum underlay scenario

机译:基于下行链路MIMO-OFDMA认知无线网络的资源分配算法频谱下层场景

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In this study, the sum throughput maximisation problem for cognitive radio networks (CRNs) based on the multiple-input multiple-output orthogonal frequency division multiple access (MIMO-OFDMA) structure has been investigated. Hence, the intention is to maximise the downlink sum throughput of the CRN in a way that the sum power of the cognitive base station (CBS) remains below the given power limit and the induced interference on each subcarrier guarantees the interference threshold level predefined by primary users. Here, due to combinatorial constraints, the complex problem should be resolved. Hence, the core purpose of this study is to prepare a novel resource assignment algorithm that tries to fulfil these combinatorial constraints simultaneously. Therefore, this study makes progress towards solving the problem theoretically based on convex optimisation framework. An optimal solution has been implemented by proposing the iterative algorithm in which an optimal level of Lagrangian coefficients is obtained. Then, because of the optimal algorithm intricacy, the two low complicated algorithms are further suggested based on the solutions of two simplified versions of the original problem. Numerical results demonstrate that the suggested algorithms improve sum throughput considerably in comparison with classical algorithm. The proposed simplified algorithms converge to the optimal solutions' performance while their complexities are desirable for practical implementation.
机译:在本研究中,已经研究了基于多输入多输入多输出正交频率分割多址(MIMO-OFDMA)结构的认知无线电网络(CRN)的总吞吐量最大化问题。因此,目的是以一种方式最大化CRN的下行链路和吞吐量,即认知基站(CBS)的总和功率保持低于给定的功率限制,并且每个子载波的感应干扰保证由主预定义的干扰阈值电平用户。这里,由于组合限制,应解决复杂问题。因此,本研究的核心目的是准备一种新的资源分配算法,该算法试图同时满足这些组合约束。因此,本研究在理论上基于凸优化框架理论上取得解决问题。通过提出获得Lagrangian系数的最佳水平的迭代算法,已经实现了最佳解决方案。然后,由于最佳算法复杂性,基于两个简化版本的原始问题的解决方案进一步提出了两个低复杂算法。数值结果表明,与经典算法相比,建议的算法显着提高了总吞吐量。所提出的简化算法会聚到最佳解决方案的性能,同时它们的复杂性是理想的实现。

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