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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network
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Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network

机译：基于能量收集的认知传感器网络中频谱感知和能量收集的联合资源分配

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摘要

The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models.
机译：当通过频谱感测检测到PU的不存在时，认知传感器（CS）可以将数据以许可给主要用户（PU）的相同频谱传输到控制中心。但是，由于CS的体积小，在恶劣环境中的部署以及长期工作，因此其电池能量受到限制。在本文中，描述了一种基于能量收集的CS，它可以感测PU并收集射频能量以提供数据传输。为了提高CS的传输性能，我们提出了在基于单个能量收集的CS和基于能量收集的认知传感器网络（CSN）的情况下，频谱感知和能量收集的联合资源分配，分别。基于提出的帧结构，我们将资源分配表述为一类联合优化问题，旨在通过共同优化感测时间，收获时间以及传感节点和收获节点的数量来最大化CS的传输速率。通过使用半搜索方法和交替方向优化，我们通过将联合优化问题转换为几个凸次优化问题来实现了次优解决方案。仿真结果表明了所提出的基于能量收集的CS和CSN模型的优势。

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