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Energy-Efficient Chance-Constrained Resource Allocation for Multicast Cognitive OFDM Network

机译:组播认知OFDM网络中的节能高效机会约束资源分配

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In this paper, an energy-efficient resource allocation problem is modeled as a chance-constrained programming for multicast cognitive orthogonal frequency division multiplexing (OFDM) network. The resource allocation is subject to constraints in service quality requirements, total power, and probabilistic interference constraint. The statistic channel state information (CSI) between cognitive-based station (CBS) and primary user (PU) is adopted to compute the interference power at the receiver of PU, and we develop an energy-efficient chance-constrained subcarrier and power allocation algorithm. Support vector machine (SVM) is employed to compute the probabilistic interference constraint. Then, the chance-constrained resource allocation problem is transformed into a deterministic resource allocation problem, and Zoutendijk’s method of feasible direction is utilized to solve it. Simulation results demonstrate that the proposed algorithm not only achieves a tradeoff between energy efficiency and satisfaction index, but also guarantees the probabilistic interference constraint very well.
机译:本文将一种节能资源分配问题建模为多播认知正交频分复用(OFDM)网络的机会受限编程。资源分配受到服务质量要求,总功率和概率干扰约束的约束。采用基于认知的基站(CBS)与主要用户(PU)之间的统计信道状态信息(CSI)来计算PU接收机处的干扰功率,并且我们开发了一种节能的机会受限子载波和功率分配算法。支持向量机(SVM)用于计算概率干扰约束。然后,将机会受限的资源分配问题转化为确定性的资源分配问题,并使用Zoutendijk的可行方向方法进行求解。仿真结果表明,该算法不仅在能效和满意度指标之间取得了平衡,而且很好地保证了概率干扰约束。

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