A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

Wael M. Bazzi; Amir Rastegarnia; Azam Khalili

doi:10.1007/s11633-014-0838-x

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

doi: 10.1007/s11633-014-0838-x

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates;
Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran;
Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

详细信息

作者简介:
Wael M. Bazzi graduated from the American University of Beirut (AUB), Lebanon in 1996. He received the M. Eng. degree from AUB in 1999 and the Ph.D. degree from the University of Waterloo, Canada, in 2001. He is currently an associate professor at the American University in Dubai. His research interests include wireless communication and networks, especially the optimization and modeling aspects of communication networks and systems E-mail: wbazzi@and.edu

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates;
Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran;
Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

摘要: In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms (also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square (LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio (SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.
- Adaptive networks /
- distributed estimation /
- least mean-square (LMS) /
- incremental cooperation /
- quality aware
Abstract: In this paper, we consider the problem of unknown parameter estimation using a set of nodes that are deployed over an area. The recently proposed distributed adaptive estimation algorithms (also known as adaptive networks) are appealing solutions to the mentioned problem when the statistical information of the underlying process is not available or it varies over time. In this paper, our goal is to develop a new incremental least-mean square (LMS) adaptive network that considers the quality of measurements collected by the nodes. Thus, we use an adaptive combination strategy which assigns each node a step size according to its quality of measurement. The adaptive combination strategy improves the robustness of the proposed algorithm to the spatial variations of signal-to-noise ratio (SNR). The performance of our algorithm is more remarkable in inhomogeneous environments when there are some nodes with low SNRs in the network. The simulation results indicate the efficiency of the proposed algorithm.
- Adaptive networks /
- distributed estimation /
- least mean-square (LMS) /
- incremental cooperation /
- quality aware

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A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

doi: 10.1007/s11633-014-0838-x

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

计量

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

doi: 10.1007/s11633-014-0838-x

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

English Abstract

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

全文HTML

目录

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

doi: 10.1007/s11633-014-0838-x

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

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出版历程

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

doi: 10.1007/s11633-014-0838-x

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates; Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran; Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

English Abstract

A Quality-aware Incremental LMS Algorithm for Distributed Adaptive Estimation

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates; Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran; Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

全文HTML

目录

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran

Electrical and Computer Engineering Department, American University in Dubai, Dubai, United Arab Emirates;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran;

Department of Electrical Engineering, Malayer University, Malayer 65719-95863, Iran