arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1102.4810cs.ITmath.IT
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高斯多址信道下基于恒模信令的分布式SNR估计

Distributed SNR Estimation using Constant Modulus Signaling over Gaussian Multiple-Access Channels

Mahesh K. Banavar, Cihan Tepedelenlioglu, Andreas Spanias

摘要:

传感器网络用于在单个时间快照中进行分布式联合均值和方差估计。传感器观察嵌入在噪声中的信号,这是相位调制使用一个恒模方案和高斯多址信道传输到一个融合中心,在那里的平均值和方差估计联合,使用渐近最小方差估计,这是解耦成简单的个人估计的平均值和方差。与放大转发方法不同,恒模相位调制方案确保了固定的发射功率、跨几个感测噪声分布的鲁棒估计以及需要从传感器到融合中心的单组传输的SNR估计。的平均值和方差的估计的性能进行评估的渐近方差,这是用来评估的SNR估计的高斯,拉普拉斯和柯西感测噪声分布的情况下的性能。对于每一个感测噪声分布,最佳相位传输参数也被确定。估计的平均值和方差的渐近相对效率进行评估。结果表明,在所考虑的噪声分布中,只有当噪声分布为高斯分布时,估计量才是渐近有效的。仿真结果证实了分析结果。

英文摘要:

A sensor network is used for distributed joint mean and variance estimation, in a single time snapshot. Sensors observe a signal embedded in noise, which are phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center, where the mean and variance are estimated jointly, using an asymptotically minimum-variance estimator, which is shown to decouple into simple individual estimators of the mean and the variance. The constant-modulus phase modulation scheme ensures a fixed transmit power, robust estimation across several sensing noise distributions, as well as an SNR estimate that requires a single set of transmissions from the sensors to the fusion center, unlike the amplify-and-forward approach. The performance of the estimators of the mean and variance are evaluated in terms of asymptotic variance, which is used to evaluate the performance of the SNR estimator in the case of Gaussian, Laplace and Cauchy sensing noise distributions. For each sensing noise distribution, the optimal phase transmission parameters are also determined. The asymptotic relative efficiency of the mean and variance estimators is evaluated. It is shown that among the noise distributions considered, the estimators are asymptotically efficient only when the noise distribution is Gaussian. Simulation results corroborate analytical results.

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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