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arXiv 1502.04099cs.ITmath.IT

能量收集认知无线电网络中的功率与频谱二维感知

Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Yanyan Zhang, Weijia Han, Di Li, Ping Zhang, Shuguang Cui

AI总结:

针对认知无线电网络频谱与能量短缺问题,提出联合频谱与收集功率的二维感知方案,基于隐输入马尔可夫模型建模主用户动态,可提升主用户检测性能并估计其发射功率,为首项相关研究。

摘要:

基于能量收集器的认知无线电是解决频谱与能量双重短缺问题的一种极具前景的方案。由于频谱接入模式与功耗模式相互关联,且从特定环境源收集的功率值具有空间相关性,新的功率维度可提供额外信息以提升频谱感知精度。本文考虑主用户的马尔可夫行为,在此基础上采用隐输入马尔可夫模型来描述系统中主用户与次用户的动态特性。据此,我们提出了一种频谱与(收集的)功率二维感知方案,以提升主用户检测性能,该方案还能够估计主用户的发射功率水平。理论与仿真结果证明了所提方案的有效性,体现为引入新的功率维度所带来的性能增益。据我们所知,这是首个在认知主用户检测问题中联合考虑频谱与功率维度的研究。

英文摘要:

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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