15 research outputs found
Compressive sensing based Bayesian sparse channel estimation for OFDM communication systems: high performance and low complexity
In orthogonal frequency division modulation (OFDM) communication systems,
channel state information (CSI) is required at receiver due to the fact that
frequency-selective fading channel leads to disgusting inter-symbol
interference (ISI) over data transmission. Broadband channel model is often
described by very few dominant channel taps and they can be probed by
compressive sensing based sparse channel estimation (SCE) methods, e.g.,
orthogonal matching pursuit algorithm, which can take the advantage of sparse
structure effectively in the channel as for prior information. However, these
developed methods are vulnerable to both noise interference and column
coherence of training signal matrix. In other words, the primary objective of
these conventional methods is to catch the dominant channel taps without a
report of posterior channel uncertainty. To improve the estimation performance,
we proposed a compressive sensing based Bayesian sparse channel estimation
(BSCE) method which can not only exploit the channel sparsity but also mitigate
the unexpected channel uncertainty without scarifying any computational
complexity. The propose method can reveal potential ambiguity among multiple
channel estimators that are ambiguous due to observation noise or correlation
interference among columns in the training matrix. Computer simulations show
that propose method can improve the estimation performance when comparing with
conventional SCE methods.Comment: 24 pages,16 figures, submitted for a journa
Variable Earns Profit: Improved Adaptive Channel Estimation using Sparse VSS-NLMS Algorithms
Accurate channel estimation is essential for broadband wireless
communications. As wireless channels often exhibit sparse structure, the
adaptive sparse channel estimation algorithms based on normalized least mean
square (NLMS) have been proposed, e.g., the zero-attracting NLMS (ZA-NLMS)
algorithm and reweighted zero-attracting NLMS (RZA-NLMS). In these NLMS-based
algorithms, the step size used to iteratively update the channel estimate is a
critical parameter to control the estimation accuracy and the convergence speed
(so the computational cost). However, invariable step-size (ISS) is usually
used in conventional algorithms, which leads to provide performance loss or/and
low convergence speed as well as high computational cost. To solve these
problems, based on the observation that large step size is preferred for fast
convergence while small step size is preferred for accurate estimation, we
propose to replace the ISS by variable step size (VSS) in conventional
NLMS-based algorithms to improve the adaptive sparse channel estimation in
terms of bit error rate (BER) and mean square error (MSE) metrics. The proposed
VSS-ZA-NLMS and VSS-RZA-NLMS algorithms adopt VSS, which can be adaptive to the
estimation error in each iteration, i.e., large step size is used in the case
of large estimation error to accelerate the convergence speed, while small step
size is used when the estimation error is small to improve the steady-state
estimation accuracy. Simulation results are provided to validate the
effectiveness of the proposed scheme.Comment: 6 pages, 9 figures, submitted for ICC201
RZA-NLMF algorithm based adaptive sparse sensing for realizing compressive sensing problems
Nonlinear sparse sensing (NSS) techniques have been adopted for realizing
compressive sensing in many applications such as Radar imaging. Unlike the NSS,
in this paper, we propose an adaptive sparse sensing (ASS) approach using
reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm
which depends on several given parameters, i.e., reweighted factor,
regularization parameter and initial step-size. First, based on the independent
assumption, Cramer Rao lower bound (CRLB) is derived as for the trademark of
performance comparisons. In addition, reweighted factor selection method is
proposed for achieving robust estimation performance. Finally, to verify the
algorithm, Monte Carlo based computer simulations are given to show that the
ASS achieves much better mean square error (MSE) performance than the NSS.Comment: 15 pages, 9 figures, submitted for journa