1,488 research outputs found
Classical and Bayesian Linear Data Estimators for Unique Word OFDM
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.Comment: Preprint, 13 page
Orthogonal Transform Multiplexing with Memoryless Nonlinearity: a Possible Alternative to Traditional Coded-Modulation Schemes
In this paper, we propose a novel joint coding-modulation technique based on
serial concatenation of orthogonal linear transform, such as discrete Fourier
transform (DFT) or Walsh-Hadamard transform (WHT), with memoryless
nonlinearity. We demonstrate that such a simple signal construction may exhibit
properties of a random code ensemble, as a result approaching channel capacity.
Our computer simulations confirm that if the decoder relies on a modified
approximate message passing algorithm, the proposed modulation technique
exhibits performance on par with state-of-the-art coded modulation schemes that
use capacity-approaching component codes. The proposed modulation scheme could
be used directly or as a pre-coder for a conventional orthogonal frequency
division multiplexing (OFDM) transmitter, resulting in a system possessing all
benefits of OFDM along with reduced peak-to-average power ratio (PAPR)
A unified approach to sparse signal processing
A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i
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