Communication systems have to cope with a variety of non-idealities. Among those, carrier frequency offset (CFO) induced impairments denote a challenging task for any system, but especially for multi-carrier schemes such as orthogonal frequency division multiplexing (OFDM). Unique Word-OFDM (UW-OFDM) is a known variant, which provides various performance benefits over conventional cyclic prefix (CP) based OFDM. In particular, UW-OFDM features excellent spectral sidelobe suppression properties and an outstanding bit error ratio performance. In this work, we expand the investigations to CFO impairments, analyze the varying effects experienced by different UW-OFDM realizations and compare it against conventional OFDM as well as single-carrier systems. Through an error analysis it is shown that compared to CP-OFDM, UW-OFDM has a significantly higher robustness against CFO. This advantage becomes even more dominant with an increasing CFO.

In this work, we consider a bit-interleaved coded modulation unique word (UW) OFDM system with turbo equalization. For such a system, only a limited performance improvement over the turbo iterations was observed in a previous work. This is because interleaving was limited to one UW-OFDM symbol, which causes dependencies between the log-likelihood ratios exchanged between the equalizer and the decoder. To overcome this issue, we propose interleaving beyond one UW-OFDM symbol. Moreover, through bit error ratio simulations we determine the minimum number of UW-OFDM symbols across which interleaving should be performed to reach the matched filter bound (MFB).

Biased estimators can outperform unbiased ones in terms of the mean square error (MSE). The best linear unbiased estimator (BLUE) fulfills the so called global conditional unbiased constraint when treated in the Bayesian framework. Recently, the component-wise conditionally unbiased linear minimum mean square error (CWCU LMMSE) estimator has been introduced. This estimator preserves a quite strong (namely the CWCU) unbiased condition which in effect sufficiently represents the intuitive view of unbiasedness. Generally, it is global conditionally biased and outperforms the BLUE in a Bayesian MSE sense. In this work we briefly recapitulate CWCU LMMSE estimation under linear model assumptions, and additionally derive the CWCU LMMSE estimator under the (only) assumption of jointly Gaussian parameters and measurements. The main intent of this work, however, is the extension of the theory of CWCU estimation to CWCU widely linear estimators. We derive the CWCU WLMMSE estimator for different model assumptions and address the analytical relationships between CWCU WLMMSE and WLMMSE estimators. The properties of the CWCU WLMMSE estimator are deduced analytically, and compared by simulation to global conditionally unbiased as well as WLMMSE counterparts with the help of a parameter estimation example and a data estimation/channel equalization application.

In this paper we consider a unique word OFDM (UW-OFDM) system with iterative detection, for which we explore two soft-input soft-output (SISO) detection algorithms: A LMMSE detector and a detector based on the generalized approximate message passing (GAMP) algorithm. For the GAMP based detector we propose additional simplifications suitable for UW-OFDM to further reduce the complexity while exhibiting only a small BER performance loss. As verified by computer simulations, both algorithms show a significant BER performance improvement over the iterations. Moreover, the GAMP based detector with the proposed simplifications outperforms the computational more complex LMMSE detector.

Unique word orthogonal frequency division multiplexing (UW-OFDM) inherently introduces a complex number Reed Solomon (RS) code. Originally, the code generator matrix of systematic coded UW-OFDM had been designed rather intuitively by minimizing the mean redundant energy. In this work we justify this approach by applying a cost function that incorporates the overall transceiver chain including a linear minimum mean square error (LMMSE) data estimator. In addition to the LMMSE estimator we investigate a nonlinear sphere detection (SD) receiver for both systematic and nonsystematic coded UW-OFDM. We study and interpret the estimators' performance and their diverse ability to exploit the redundant energy.