Classical and Bayesian Linear Data Estimators for Unique Word OFDM

Details Classical and Bayesian Linear Data Estimators for Unique Word OFDM Classical and Bayesian Linear Data Estimators for Unique Word OFDM

2012-02-07

arxiv.org/abs/1202.1398v1

IEEE Transactions on Signal Processing, Vol. 59, pp. 6073-6085, Dec. 2011

Computer Science

Information Theory

Preprint, 13 pages

Scientific paper

10.1109/TSP.2011.2164912

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.

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