6,779 research outputs found
Fast and Robust Parametric Estimation of Jointly Sparse Channels
We consider the joint estimation of multipath channels obtained with a set of
receiving antennas and uniformly probed in the frequency domain. This scenario
fits most of the modern outdoor communication protocols for mobile access or
digital broadcasting among others.
Such channels verify a Sparse Common Support property (SCS) which was used in
a previous paper to propose a Finite Rate of Innovation (FRI) based sampling
and estimation algorithm. In this contribution we improve the robustness and
computational complexity aspects of this algorithm. The method is based on
projection in Krylov subspaces to improve complexity and a new criterion called
the Partial Effective Rank (PER) to estimate the level of sparsity to gain
robustness.
If P antennas measure a K-multipath channel with N uniformly sampled
measurements per channel, the algorithm possesses an O(KPNlogN) complexity and
an O(KPN) memory footprint instead of O(PN^3) and O(PN^2) for the direct
implementation, making it suitable for K << N. The sparsity is estimated online
based on the PER, and the algorithm therefore has a sense of introspection
being able to relinquish sparsity if it is lacking. The estimation performances
are tested on field measurements with synthetic AWGN, and the proposed
algorithm outperforms non-sparse reconstruction in the medium to low SNR range
(< 0dB), increasing the rate of successful symbol decodings by 1/10th in
average, and 1/3rd in the best case. The experiments also show that the
algorithm does not perform worse than a non-sparse estimation algorithm in
non-sparse operating conditions, since it may fall-back to it if the PER
criterion does not detect a sufficient level of sparsity.
The algorithm is also tested against a method assuming a "discrete" sparsity
model as in Compressed Sensing (CS). The conducted test indicates a trade-off
between speed and accuracy.Comment: 11 pages, 9 figures, submitted to IEEE JETCAS special issue on
Compressed Sensing, Sep. 201
An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation
In this work we design a receiver that iteratively passes soft information
between the channel estimation and data decoding stages. The receiver
incorporates sparsity-based parametric channel estimation. State-of-the-art
sparsity-based iterative receivers simplify the channel estimation problem by
restricting the multipath delays to a grid. Our receiver does not impose such a
restriction. As a result it does not suffer from the leakage effect, which
destroys sparsity. Communication at near capacity rates in high SNR requires a
large modulation order. Due to the close proximity of modulation symbols in
such systems, the grid-based approximation is of insufficient accuracy. We show
numerically that a state-of-the-art iterative receiver with grid-based sparse
channel estimation exhibits a bit-error-rate floor in the high SNR regime. On
the contrary, our receiver performs very close to the perfect channel state
information bound for all SNR values. We also demonstrate both theoretically
and numerically that parametric channel estimation works well in dense
channels, i.e., when the number of multipath components is large and each
individual component cannot be resolved.Comment: Major revision, accepted for IEEE Transactions on Signal Processin
DeepMatching: Hierarchical Deformable Dense Matching
We introduce a novel matching algorithm, called DeepMatching, to compute
dense correspondences between images. DeepMatching relies on a hierarchical,
multi-layer, correlational architecture designed for matching images and was
inspired by deep convolutional approaches. The proposed matching algorithm can
handle non-rigid deformations and repetitive textures and efficiently
determines dense correspondences in the presence of significant changes between
images. We evaluate the performance of DeepMatching, in comparison with
state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al
2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013)
datasets. DeepMatching outperforms the state-of-the-art algorithms and shows
excellent results in particular for repetitive textures.We also propose a
method for estimating optical flow, called DeepFlow, by integrating
DeepMatching in the large displacement optical flow (LDOF) approach of Brox and
Malik (2011). Compared to existing matching algorithms, additional robustness
to large displacements and complex motion is obtained thanks to our matching
approach. DeepFlow obtains competitive performance on public benchmarks for
optical flow estimation
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