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Construction of Time-Space Radio Environment Database using HMM for Cooperative Sensing

机译:基于HMM的协同感知时空无线电环境数据库构建

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In recent years, many researchers focus on a measurement-based radio environment database (RED) that utilizes the actual received signal power obtained by spectrum sensing as an enabler for an efficient frequency sharing. In this paper, in an environment where multiple transmitters are switched ON/OFF, each sensor is distributed in the environment by cooperative sensing and acquires time series data. Moreover, each sensor learns using the Hidden Markov Model (HMM) on the acquired time series data, thereby estimating the parameter of the receivable transmitter. After collecting estimation results by HMM in each sensor, we propose to use sensor selection for estimation of time parameters (channel occupancy rate, average ON/OFF interval) and multiple imputation for estimation of spatial parameters (pathloss exponent, transmit power). By the proposed method, it is possible to construct a highly accurate time-space RED. The simulation results confirm the effectiveness of the proposed method.
机译:近年来,许多研究人员专注于基于测量的无线电环境数据库(RED),该数据库利用频谱感测获得的实际接收信号功率作为有效频率共享的促成因素。在本文中,在多个发射器被打开/关闭的环境中,每个传感器通过协作感测分布在环境中,并获取时间序列数据。此外,每个传感器在获取的时间序列数据上使用隐马尔可夫模型(HMM)进行学习,从而估算可接收发射机的参数。在通过HMM收集每个传感器的估计结果后,我们建议使用传感器选择来估计时间参数(信道占用率,平均ON / OFF间隔),并使用多重插补来估计空间参数(路径损耗指数,发射功率)。通过所提出的方法,可以构造高精度的时空RED。仿真结果证实了该方法的有效性。

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