Neural approximations of analog joint source-channel coding

An 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. Linear MMSE encoders and decoders, parametrically optimized in encoders' gains, Shannon–Kotel'nikov mappings, and nonlinear parametric functional approximators (neural networks) are investigated and numerically compared, highlighting subtle differences in sensitivity and achievable performance.