Interference Efficiency: A New Metric to Analyze the Performance of Cognitive Radio Networks

Interference Efficiency: A New Metric to Analyze the Performance of Cognitive Radio Networks
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DOI:
10.1109/twc.2016.2647252
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发表时间:
2017-04
影响因子:
10.4
通讯作者:
Mohammad Robat Mili;Leila Musavian
Mohammad Robat Mili;Leila Musavian
中科院分区:
计算机科学1区
文献类型:
--
作者:
Mohammad Robat Mili;Leila Musavian

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本文提出并分析了一种新的性能度量,称为干扰效率,它反映了在底层认知无线电网络(CRN)中施加给主用户(PU)的单位干扰能量的传输比特数。具体地说,我们开发了一个框架来最大化具有多个次级用户(SU)的CRN的干扰效率,同时满足SU的平均干扰功率、总发射功率和最小遍历速率的目标约束。在这样做的过程中,我们建立了一个多目标优化问题(MOP),目标是最大化SU的遍历总和率,并最小化主接收器上的平均干扰功率。我们首先使用加权和方法将其转化为单目标问题(SOP)来求解MOP。考虑到不同场景下SU发射机的信道状态信息(CSI)可用性,我们研究了CSI对SU性能和功率分配的影响。当完全CSI可用时,所形成的SOP是非凸的,并且使用增广罚函数法(也称为乘子法)来求解。当只有SU发射机和PU接收机之间的信道增益的统计信息可用时,使用拉格朗日优化来求解SOP。数值结果证实了我们的理论分析。
In this paper, we develop and analyze a novel performance metric, called interference efficiency, which shows the number of transmitted bits per unit of interference energy imposed on the primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we develop a framework to maximize the interference efficiency of a CRN with multiple secondary users (SUs) while satisfying target constraints on the average interference power, total transmit power, and minimum ergodic rate for the SUs. In doing so, we formulate a multiobjective optimization problem (MOP) that aims to maximize ergodic sum rate of SUs and to minimize average interference power on the primary receiver. We solve the MOP by first transferring it into a single objective problem (SOP) using a weighted sum method. Considering different scenarios in terms of channel state information (CSI) availability to the SU transmitter, we investigate the effect of CSI on the performance and power allocation of the SUs. When full CSI is available, the formulated SOP is nonconvex and is solved using augmented penalty method (also known as the method of multiplier). When only statistical information of the channel gains between the SU transmitters and the PU receiver is available, the SOP is solved using Lagrangian optimization. Numerical results are conducted to corroborate our theoretical analysis.