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Signal Estimation in Underlay Cognitive Networks for Industrial Internet of Things

机译：工业互联网界面认知网络中的信号估计

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摘要

Underlay cognitive radio (CR) holds the promise to address spectrum scarcity and let industrial wireless sensor networks obtain spectrum extension from shared frequency band resources. However, underlay CR devices should be capable of properly adjusting wireless transmission parameters according to the sensing of wireless environments. To realize the goal, in this article, two different signal-to-noise ratio (SNR) estimation methods are proposed for time-frequency overlapped signal estimations in the underlay CR-based industrial Internet of Things (IoT). In the first method, normalized higher order cumulant equations and the theoretical value of normalized higher order cumulants are adopted to estimate the SNR of component signals and the SNR of received signals. In the second one, the power of each component signals and the received signals is estimated based on the second-order time-varying moments. For the performance analysis, the Cramer-Rao lower bound of the SNR estimation for the time-frequency overlapped signals is derived. Simulation results show that the proposed method based on normalized higher order cumulants not only can effectively estimate the SNR of the time-frequency overlapped signals, but also has the strong robustness to the spectrum overlapped rate and the hybrid power ratio. The proposed method with second-order time-varying moments is able to accurately estimate the SNR of the time-frequency overlapped signals effectively, especially in the low-SNR region. These features are extremely useful in the industrial IoT, which usually operate in low-SNR regimes.
机译：底层认知无线电（CR）占据解决频谱稀缺性的承诺，并且让工业无线传感器网络从共享频带资源获得频谱扩展。然而，底层CR器件应该能够根据无线环境的感测来适当地调整无线传输参数。为了实现目标，在本文中，提出了两种不同的信噪比（SNR）估计方法，用于基于底层CR的工业互联网（物联网）中的时频重叠信号估计。在第一种方法中，采用归一化高阶累积累积方程和归一化高阶累积物的理论值来估计分量信号的SNR和接收信号的SNR。在第二个中，基于二阶时刻估计每个分量信号和接收信号的功率。对于性能分析，导出了用于时频重叠信号的SNR估计的CRAMER-RAO下限。仿真结果表明，基于归一化高阶累积物的提出方法不仅可以有效地估计时频重叠信号的SNR，而且还具有对频谱重叠速率和混合动力比的强鲁棒性。具有二阶时变矩的所提出的方法能够有效地精确地估计时频重叠信号的SNR，尤其是在低SNR区域中。这些功能在工业物联网中非常有用，通常在低SNR制度中运行。

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