What makes a good role model

TL;DR

This paper introduces the role model strategy for estimator design, demonstrating its optimality under certain conditions, and illustrates its application through examples and numerical methods, with potential uses beyond communications.

Contribution

It presents the role model strategy as a novel approach for estimator design that achieves optimal Bayesian estimators under Markov conditions, with practical implementation methods.

Findings

The role model strategy yields optimal Bayesian estimators when Markov conditions are met.

Numerical solutions via convex programming effectively implement the strategy.

Potential applications extend beyond communication systems.

Abstract

The role model strategy is introduced as a method for designing an estimator by approaching the output of a superior estimator that has better input observations. This strategy is shown to yield the optimal Bayesian estimator when a Markov condition is fulfilled. Two examples involving simple channels are given to illustrate its use. The strategy is combined with time averaging to construct a statistical model by numerically solving a convex program. The role model strategy was developed in the context of low complexity decoder design for iterative decoding. Potential applications outside the field of communications are discussed.