1,606 research outputs found
Wavelet Based Semi-blind Channel Estimation For Multiband OFDM
This paper introduces an expectation-maximization (EM) algorithm within a
wavelet domain Bayesian framework for semi-blind channel estimation of
multiband OFDM based UWB communications. A prior distribution is chosen for the
wavelet coefficients of the unknown channel impulse response in order to model
a sparseness property of the wavelet representation. This prior yields, in
maximum a posteriori estimation, a thresholding rule within the EM algorithm.
We particularly focus on reducing the number of estimated parameters by
iteratively discarding ``unsignificant'' wavelet coefficients from the
estimation process. Simulation results using UWB channels issued from both
models and measurements show that under sparsity conditions, the proposed
algorithm outperforms pilot based channel estimation in terms of mean square
error and bit error rate and enhances the estimation accuracy with less
computational complexity than traditional semi-blind methods
Estimation of Sparse MIMO Channels with Common Support
We consider the problem of estimating sparse communication channels in the
MIMO context. In small to medium bandwidth communications, as in the current
standards for OFDM and CDMA communication systems (with bandwidth up to 20
MHz), such channels are individually sparse and at the same time share a common
support set. Since the underlying physical channels are inherently
continuous-time, we propose a parametric sparse estimation technique based on
finite rate of innovation (FRI) principles. Parametric estimation is especially
relevant to MIMO communications as it allows for a robust estimation and
concise description of the channels. The core of the algorithm is a
generalization of conventional spectral estimation methods to multiple input
signals with common support. We show the application of our technique for
channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink
(Walsh-Hadamard coded schemes). In the presence of additive white Gaussian
noise, theoretical lower bounds on the estimation of SCS channel parameters in
Rayleigh fading conditions are derived. Finally, an analytical spatial channel
model is derived, and simulations on this model in the OFDM setting show the
symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR)
compared to standard non-parametric methods - e.g. lowpass interpolation.Comment: 12 pages / 7 figures. Submitted to IEEE Transactions on Communicatio
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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