Classical and Bayesian Linear Data Estimators for Unique Word OFDM

TL;DR

This paper introduces and compares classical and Bayesian linear estimators for Unique Word OFDM, a novel signaling method that improves bit error rate by exploiting correlations in the guard interval, with detailed complexity and performance analysis.

Contribution

It develops new linear estimators tailored for UW-OFDM, including complexity-optimized versions, and evaluates their performance in various channel conditions.

Findings

Bayesian estimators outperform classical ones in noise resilience.

Complexity analysis shows optimized estimators require fewer multiplications.

Performance evaluations demonstrate improved BER in multipath channels.

Abstract

Unique word - orthogonal frequency division multiplexing (UW-OFDM) is a novel OFDM signaling concept, where the guard interval is built of a deterministic sequence - the so-called unique word - instead of the conventional random cyclic prefix. In contrast to previous attempts with deterministic sequences in the guard interval the addressed UW-OFDM signaling approach introduces correlations between the subcarrier symbols, which can be exploited by the receiver in order to improve the bit error ratio performance. In this paper we develop several linear data estimators specifically designed for UW-OFDM, some based on classical and some based on Bayesian estimation theory. Furthermore, we derive complexity optimized versions of these estimators, and we study their individual complex multiplication count in detail. Finally, we evaluate the estimators' performance for the additive white Gaussian noise channel as well as for selected indoor multipath channel scenarios.