Type Based Estimation Over Multiaccess Channels.

We study the problem of communicating sensor readings over a Gaussian multiaccess channel. We focus on the scenario that each sensor observes a single random variable and transmits it using certain signaling in a shared channel. The objective is the design of channel waveforms (i.e., the signal constellation) to facilitate the estimation of field parameters from the channel output. We propose a communication scheme in which sensors transmit according to the type of their observations—type-based multiple access (TBMA)—and show that the TBMA is asymptotically optimal in the limit of large number of sensors if the sensor channel-gains are identical. In particular, we show that TBMA together with a variant of the maximum-likelihood estimator achieves the Cramer-Rao bound asymptotically. We then extend the asymptotic analysis of TBMA to fading channels and compare the performance of TBMA with other orthogonal allocation methods such as time-division multiple access. [ABSTRACT FROM AUTHOR]