CSC   24412
CENTRO DE SIMULACION COMPUTACIONAL PARA APLICACIONES TECNOLOGICAS
Unidad Ejecutora - UE
artículos
Título:
Optimal Resource Allocation for Detection of a Gaussian Process using a MAC in WSNs
Autor/es:
JUAN MAYA; LEONARDO REY VEGA; CECILIA GALARZA
Revista:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Editorial:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Referencias:
Lugar: New York; Año: 2015 vol. 63 p. 2057 - 2057
ISSN:
1053-587X
Resumen:
We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman-Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considering an energy constraint on each node transmission and a limited amount of channel uses, we compute the miss error exponent of the proposed scheme using Large Deviation Theory (LDT) and show that the proposed strategy is asymptotically optimal (when the number of sensors approaches infinity) among linear orthogonal schemes. We also show that the proposed scheme obtains meaningful energy saving in the low signal-to-noise ratio regime, which is the typical scenario of WSNs. Finally, a Monte Carlo simulation of a 2-dimensional process in space validates the analytical results.
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