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首页> 外文期刊>IEEE Transactions on Signal Processing >Practical Issues in Estimation Over Multiaccess Fading Channels With TBMA Wireless Sensor Networks
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Practical Issues in Estimation Over Multiaccess Fading Channels With TBMA Wireless Sensor Networks

机译:使用TBMA无线传感器网络进行多址衰落信道估计中的实际问题

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Practical issues in histogram and parameter estimation over fading channels with type-based multiple access (TBMA) sensor networks is addressed in this paper, where the parameter of interest is estimated through the histogram, or type, of the observations. Existing histogram estimators in the literature require the transmitted signal waveforms to be orthogonal to represent different observations. If the fading channels from the sensors to the fusion center are zero mean, channel state information (CSI) is required at the sensor side. However, in practice, the interference between the orthogonal waveforms, and channel estimation error (CEE) cannot be avoided. How these practical issues affect the histogram and parameter estimation is discussed in this paper. A unified framework for histogram and parameter estimation in the presence of interference and imperfect CSI is proposed. In the interference-free case, a novel histogram estimator is proposed, which does not require the knowledge of the channel statistics at the fusion center, and yields an asymptotically optimal estimator. This approach is then generalized to the presence of interference. The existing estimators without the knowledge of interference statistics are shown to be biased, which motivates the proposed asymptotically optimal estimators that utilize interference statistics. Moreover, the performance of the asymptotically optimal estimators are shown to deteriorate when the waveforms are not orthogonal. Simulation results corroborate our analysis.
机译:本文解决了基于类型的多址访问(TBMA)传感器网络的直方图和衰落信道上的参数估计中的实际问题,其中感兴趣的参数是通过观测的直方图或类型来估计的。文献中现有的直方图估计器要求发射的信号波形正交以代表不同的观察结果。如果从传感器到融合中心的衰落信道均值为零,则在传感器侧需要信道状态信息(CSI)。但是,实际上,不能避免正交波形之间的干扰以及信道估计误差(CEE)。本文讨论了这些实际问题如何影响直方图和参数估计。提出了在存在干扰和不完善CSI的情况下直方图和参数估计的统一框架。在无干扰的情况下,提出了一种新颖的直方图估计器,该估计器不需要了解融合中心的信道统计信息,而是产生了一个渐近最优估计器。然后将这种方法推广到存在干扰。没有干扰统计知识的现有估计器显示为有偏差的,这激发了提出的利用干扰统计量的渐近最优估计器。此外,当波形不正交时,渐近最优估计器的性能会变差。仿真结果证实了我们的分析。

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