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Green Energy-Powered Allocations for OFDM-based Cognitive Radio Networks

机译:基于OFDM的认知无线电网络的绿色能量供电拨款

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In this paper, we focus on a green energy-powered allocations for OFDM-based Cognitive Radio (CR) networks composed of the secondary users (SUs) and the primary users (PUs). We analyse a green energy-efficient (EE) power allocation problem at the SU level under feasibility constraints. When the SU is allowed to transmit, these constraints guarantee a minimum SU's Quality of Service (QoS) level, a maximum average transmit power and the protection of the PUs. The green energy-powered allocation problem under study is the weighted difference between overall achievable rate and power consumption. Since the opposing character of the constraints, it is difficult to solve this green energy-powered allocation problem via conventional optimization tools. Therefore, we propose an efficient iterative water-filling algorithm in order to find the optimal power allocation.
机译:在本文中,我们专注于由二级用户(SUS)和主要用户(PU)组成的基于OFDM的认知无线电(CR)网络的绿色能量供电。 我们在可行性约束下分析了SU水平的绿色节能(EE)电力分配问题。 当SU被允许传输时,这些约束确保了最小SU的服务质量(QoS)级别,最大的平均传输功率和PUS的保护。 正在研究的绿色能源通力分配问题是总体可实现率和功耗之间的加权差异。 由于限制的相反特性,因此难以通过传统的优化工具解决该绿色能量供电的分配问题。 因此,我们提出了一种高效的迭代水填充算法,以找到最佳功率分配。

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