Distributed spectrum estimation based on alternating mixed discrete-continuous adaptation

Tamara Guerra Miller; Songcen Xu; Rodrigo C. De Lamare; H. Vincent Poor

doi:10.1109/LSP.2016.2539328

Distributed spectrum estimation based on alternating mixed discrete-continuous adaptation

Tamara Guerra Miller, Songcen Xu, Rodrigo C. De Lamare, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

This letter proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The proposed algorithm exhibits improved performance in terms of mean square deviation and power spectrum estimation accuracy. Numerical results show that the DAMDC algorithm achieves excellent performance.

Original language	English (US)
Article number	7428768
Pages (from-to)	551-555
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	23
Issue number	4
DOIs	https://doi.org/10.1109/LSP.2016.2539328
State	Published - Apr 2016

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Keywords

Distributed processing
diffusion-LMS
oracle Algorithm
sparsity-aware algorithms
spectrum estimation

Access to Document

10.1109/LSP.2016.2539328

Cite this

@article{db32934dcf254682a4b8782cb26c6329,

title = "Distributed spectrum estimation based on alternating mixed discrete-continuous adaptation",

abstract = "This letter proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The proposed algorithm exhibits improved performance in terms of mean square deviation and power spectrum estimation accuracy. Numerical results show that the DAMDC algorithm achieves excellent performance.",

keywords = "Distributed processing, diffusion-LMS, oracle Algorithm, sparsity-aware algorithms, spectrum estimation",

author = "Miller, {Tamara Guerra} and Songcen Xu and {De Lamare}, {Rodrigo C.} and Poor, {H. Vincent}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Personal use is permitted.",

year = "2016",

month = apr,

doi = "10.1109/LSP.2016.2539328",

language = "English (US)",

volume = "23",

pages = "551--555",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

TY - JOUR

T1 - Distributed spectrum estimation based on alternating mixed discrete-continuous adaptation

AU - Miller, Tamara Guerra

AU - Xu, Songcen

AU - De Lamare, Rodrigo C.

AU - Poor, H. Vincent

PY - 2016/4

Y1 - 2016/4

N2 - This letter proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The proposed algorithm exhibits improved performance in terms of mean square deviation and power spectrum estimation accuracy. Numerical results show that the DAMDC algorithm achieves excellent performance.

AB - This letter proposes a distributed alternating mixed discrete-continuous (DAMDC) algorithm to approach the oracle algorithm based on the diffusion strategy for parameter and spectrum estimation over sensor networks. A least mean squares (LMS) type algorithm that obtains the oracle matrix adaptively is developed and compared with the existing sparsity-aware and conventional algorithms. The proposed algorithm exhibits improved performance in terms of mean square deviation and power spectrum estimation accuracy. Numerical results show that the DAMDC algorithm achieves excellent performance.

KW - Distributed processing

KW - diffusion-LMS

KW - oracle Algorithm

KW - sparsity-aware algorithms

KW - spectrum estimation

UR - http://www.scopus.com/inward/record.url?scp=84964374546&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964374546&partnerID=8YFLogxK

U2 - 10.1109/LSP.2016.2539328

DO - 10.1109/LSP.2016.2539328

M3 - Article

AN - SCOPUS:84964374546

SN - 1070-9908

VL - 23

SP - 551

EP - 555

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 4

M1 - 7428768

ER -

Distributed spectrum estimation based on alternating mixed discrete-continuous adaptation

Abstract

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this