Neural Approximations of Analog 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.