2,957 research outputs found
Recovering Jointly Sparse Signals via Joint Basis Pursuit
This work considers recovery of signals that are sparse over two bases. For
instance, a signal might be sparse in both time and frequency, or a matrix can
be low rank and sparse simultaneously. To facilitate recovery, we consider
minimizing the sum of the -norms that correspond to each basis, which
is a tractable convex approach. We find novel optimality conditions which
indicates a gain over traditional approaches where minimization is
done over only one basis. Next, we analyze these optimality conditions for the
particular case of time-frequency bases. Denoting sparsity in the first and
second bases by respectively, we show that, for a general class of
signals, using this approach, one requires as small as
measurements for successful recovery hence
overcoming the classical requirement of
for
minimization when . Extensive simulations show that, our
analysis is approximately tight.Comment: 8 pages, 1 figure, submitted to ISIT 201
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
To fully utilize the spatial multiplexing gains or array gains of massive
MIMO, the channel state information must be obtained at the transmitter side
(CSIT). However, conventional CSIT estimation approaches are not suitable for
FDD massive MIMO systems because of the overwhelming training and feedback
overhead. In this paper, we consider multi-user massive MIMO systems and deploy
the compressive sensing (CS) technique to reduce the training as well as the
feedback overhead in the CSIT estimation. The multi-user massive MIMO systems
exhibits a hidden joint sparsity structure in the user channel matrices due to
the shared local scatterers in the physical propagation environment. As such,
instead of naively applying the conventional CS to the CSIT estimation, we
propose a distributed compressive CSIT estimation scheme so that the compressed
measurements are observed at the users locally, while the CSIT recovery is
performed at the base station jointly. A joint orthogonal matching pursuit
recovery algorithm is proposed to perform the CSIT recovery, with the
capability of exploiting the hidden joint sparsity in the user channel
matrices. We analyze the obtained CSIT quality in terms of the normalized mean
absolute error, and through the closed-form expressions, we obtain simple
insights into how the joint channel sparsity can be exploited to improve the
CSIT recovery performance.Comment: 16 double-column pages, accepted for publication in IEEE Transactions
on Signal Processin
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
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