A maximum entropy approach to OFDM channel estimation

TL;DR

This paper introduces a Bayesian maximum entropy framework for OFDM channel estimation that adapts to varying prior knowledge and exploits time-frequency dimensions, demonstrating superior or comparable performance to classical methods.

Contribution

It presents a novel Bayesian approach based on Jaynes' maximum entropy principle for OFDM channel estimation, accommodating different levels of prior knowledge and optimizing performance.

Findings

Simulations confirm the optimality of the proposed method.

The approach outperforms classical estimators in various scenarios.

The framework effectively utilizes time-frequency information.

Abstract

In this work, a new Bayesian framework for OFDM channel estimation is proposed. Using Jaynes' maximum entropy principle to derive prior information, we successively tackle the situations when only the channel delay spread is a priori known, then when it is not known. Exploitation of the time-frequency dimensions are also considered in this framework, to derive the optimal channel estimation associated to some performance measure under any state of knowledge. Simulations corroborate the optimality claim and always prove as good or better in performance than classical estimators.