A distributed estimation setting is considered, where a number of sensors transmit their observations of a physical phenomenon, described by one or more random variables, to a sink over noisy communication channels. The goal is to minimize a quadratic distortion measure (Minimum Mean Square Error - MMSE) under a global power constraint on the sensors' transmissions. Both linear MMSE encoders and decoders, parametrically optimized in encoders' gains, and non-linear parametric functional approximators (neural networks) are investigated and numerically compared, highlighting subtle differences in sensitivity and achievable performance.
Linear and non-linear montecarlo approximations of analog joint source-channel coding under generic probability distributions
DAVOLI, FRANCO;
2014-01-01
Abstract
A distributed estimation setting is considered, where a number of sensors transmit their observations of a physical phenomenon, described by one or more random variables, to a sink over noisy communication channels. The goal is to minimize a quadratic distortion measure (Minimum Mean Square Error - MMSE) under a global power constraint on the sensors' transmissions. Both linear MMSE encoders and decoders, parametrically optimized in encoders' gains, and non-linear parametric functional approximators (neural networks) are investigated and numerically compared, highlighting subtle differences in sensitivity and achievable performance.
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