Conference paper Open Access

# Distributed Massive MIMO for Estimation of a Correlated Source Vector in Sensor Networks

Serra, Jordi; Pubill, David; Verikoukis, Christos

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<dc:creator>Serra, Jordi</dc:creator>
<dc:creator>Pubill, David</dc:creator>
<dc:creator>Verikoukis, Christos</dc:creator>
<dc:date>2017-12-18</dc:date>
<dc:description>Distributed massive MIMO (DM-MIMO) systems are a key enabler to improve the energy efficiency (EE) in future wireless networks. Thereby, herein this architecture is considered
for the estimation of a correlated source vector in wireless sensor networks (WSN), where each sensor node amplifies and forwards its observation through a coherent Multiple Access Channel
(MAC) channel. Namely, the fusion centre (FC) consists of a large number of distributed single antenna access points (AP) connected through a backhaul network to a central processing
unit (CPU), where a Linear Minimum Mean Square Error (LMMSE) estimation is computed. Within this setting the exact and an approximated MSE, obtained by the LMMSE estimation,
are derived. Bearing in mind these results, we address the design of the optimal power allocation, at each sensor node, to minimize the total transmitted power subject to an MSE estimation
constraint. The approximation of the MSE paves the way to cast the optimal allocation problem as a Semidefinite Programming Problem (SDP). Finally, the numerical simulations show that our
system permits to reduce significantly the total transmitted power compared to related work architectures proposing a massive MIMO system where all the antennas are collocated at the FC.</dc:description>
<dc:description>Grant numbers : This work has been partially supported by the CellFive project funded by the Spanish Ministry of Economy, Industry and Competitiveness with grant TEC2014-60130-P.© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</dc:description>
<dc:identifier>https://zenodo.org/record/1161201</dc:identifier>
<dc:identifier>10.5281/zenodo.1161201</dc:identifier>
<dc:identifier>oai:zenodo.org:1161201</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>info:eu-repo/grantAgreement/EC/H2020/692480/</dc:relation>
<dc:relation>doi:10.5281/zenodo.1161200</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:subject>Distributed Massive MIMO,</dc:subject>
<dc:subject>power allocation</dc:subject>
<dc:subject>estimation</dc:subject>
<dc:subject>sensor networks</dc:subject>
<dc:title>Distributed Massive MIMO for Estimation of a Correlated Source Vector in Sensor Networks</dc:title>
<dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
<dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>

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