Title
Massive MIMO for decentralized estimation over coherent multiple access channels
Abstract
We consider a decentralized multisensor estimation problem where L sensor nodes observe noisy versions of a possibly correlated random source. The sensors amplify and forward their observations over a fading coherent multiple access channel (MAC) to a fusion center (FC). The FC is equipped with a large array of N antennas, and adopts a minimum mean square error (MMSE) approach for estimating the source. We optimize the amplification factor (or equivalently transmission power) at each sensor node in two different scenarios: 1) with the objective of total power minimization subject to mean square error (MSE) of source estimation constraint, and 2) with the objective of minimizing MSE subject to total power constraint. For this purpose, we apply an asymptotic approximation based on the massive multiple-input-multiple-output (MIMO) favorable propagation condition (when L ≪ N). We use convex optimization techniques to solve for the optimal sensor power allocation in 1) and 2). In 1), we show that the total power consumption at the sensors decays as 1/N, replicating the power savings obtained in Massive MIMO mobile communications literature. Through numerical studies, we also illustrate the superiority of the proposed optimal power allocation methods over uniform power allocation.
Year
DOI
Venue
2015
10.1109/SPAWC.2015.7227036
2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
Keywords
Field
DocType
Decentralized estimation,Massive MIMO,Coherent MAC,Convex optimization,Power allocation
Sensor node,Mathematical optimization,Wireless,Fading,Computer science,Minimum mean square error,MIMO,Mean squared error,Electronic engineering,Fusion center,Convex optimization
Conference
ISSN
Citations 
PageRank 
1948-3244
4
0.45
References 
Authors
16
4
Name
Order
Citations
PageRank
Amirpasha Shirazinia1626.90
Subhrakanti Dey296668.68
Domenico Ciuonzo359942.44
Pierluigi Salvo Rossi432827.27