52 research outputs found
Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation
The design of message passing algorithms on factor graphs has been proven to
be an effective manner to implement channel estimation in wireless
communication systems. In Bayesian approaches, a prior probability model that
accurately matches the channel characteristics can effectively improve
estimation performance. In this work, we study the channel estimation problem
in a frequency division duplexing (FDD) downlink massive multiple-input
multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)
system. As the prior probability, we propose the Markov chain two-state
Gaussian mixture with large variance difference (TSGM-LVD) model to exploit the
structured sparsity in the angle-frequency domain of the massive MIMO-OFDM
channel. In addition, we present a new method to derive the hybrid message
passing (HMP) rule, which can calculate the message with mixed linear and
non-linear model. To the best of the authors' knowledge, we are the first to
apply the HMP rule to practical communication systems, designing the
HMP-TSGM-LVD algorithm under the structured turbo-compressed sensing (STCS)
framework. Simulation results demonstrate that the proposed HMP-TSGM-LVD
algorithm converges faster and outperforms its counterparts under a wide range
of simulation settings
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
Iterative frequency domain equalization with generalized approximate message passing
An iterative frequency domain equalization approach for coded single-carrier block transmissions over frequency selective channels is developed by using the recently proposed generalized approximate message passing (GAMP) algorithm. Compared with the low-complexity iterative frequency domain linear minimum mean square error (FD-LMMSE) equalization, the proposed approach can achieve significant performance gain with slight complexity increase
- …