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两波漫射功率衰落模型的联合参数估计

Joint Parameter Estimation for the Two-Wave with Diffuse Power Fading Model.

作者信息

Lopez-Fernandez Jesus, Moreno-Pozas Laureano, Lopez-Martinez Francisco Javier, Martos-Naya Eduardo

机构信息

Departamento de Ingeniería de Comunicaciones, ETS Ingeniería de Telecomunicación, Universidad de Málaga, Málaga 29071, Spain.

出版信息

Sensors (Basel). 2016 Jun 30;16(7):1014. doi: 10.3390/s16071014.

DOI:10.3390/s16071014
PMID:27376293
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4970064/
Abstract

Wireless sensor networks deployed within metallic cavities are known to suffer from a very severe fading, even in strong line-of-sight propagation conditions. This behavior is well-captured by the Two-Wave with Diffuse Power (TWDP) fading distribution, which shows great fit to field measurements in such scenarios. In this paper, we address the joint estimation of the parameters K and Δ that characterize the TWDP fading model, based on the observation of the received signal envelope. We use a moment-based approach to derive closed-form expressions for the estimators of K and Δ, as well as closed-form expressions for their asymptotic variance. Results show that the estimation error is close to the Cramer-Rao lower bound for a wide range of values of the parameters K and Δ. The performance degradation due to a finite number of observations is also analyzed.

摘要

已知部署在金属腔内的无线传感器网络即使在强视距传播条件下也会遭受非常严重的衰落。两波扩散功率(TWDP)衰落分布很好地捕捉了这种行为,它在这种场景下与现场测量结果非常吻合。在本文中,我们基于接收信号包络的观测,解决了表征TWDP衰落模型的参数K和Δ的联合估计问题。我们使用基于矩的方法来推导K和Δ估计器的闭式表达式,以及它们渐近方差的闭式表达式。结果表明,在参数K和Δ的广泛取值范围内,估计误差接近克拉美-罗下界。还分析了由于观测次数有限导致的性能下降。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/c0ad6babceba/sensors-16-01014-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/5531f0c29579/sensors-16-01014-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/7257f41b5947/sensors-16-01014-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/0ed6e2fc9dba/sensors-16-01014-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/e20c7348d6f2/sensors-16-01014-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/5b146933c47c/sensors-16-01014-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/2e8cea49f0c6/sensors-16-01014-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/08eeb21327e2/sensors-16-01014-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/c0ad6babceba/sensors-16-01014-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/5531f0c29579/sensors-16-01014-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/7257f41b5947/sensors-16-01014-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/0ed6e2fc9dba/sensors-16-01014-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/e20c7348d6f2/sensors-16-01014-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/5b146933c47c/sensors-16-01014-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/2e8cea49f0c6/sensors-16-01014-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/08eeb21327e2/sensors-16-01014-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8bf/4970064/c0ad6babceba/sensors-16-01014-g008.jpg

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引用本文的文献

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Sensors (Basel). 2022 Jan 20;22(3):774. doi: 10.3390/s22030774.