39,258 research outputs found
Compressive Sensing for Feedback Reduction in MIMO Broadcast Channels
We propose a generalized feedback model and compressive sensing based
opportunistic feedback schemes for feedback resource reduction in MIMO
Broadcast Channels under the assumption that both uplink and downlink channels
undergo block Rayleigh fading. Feedback resources are shared and are
opportunistically accessed by users who are strong, i.e. users whose channel
quality information is above a certain fixed threshold. Strong users send same
feedback information on all shared channels. They are identified by the base
station via compressive sensing. Both analog and digital feedbacks are
considered. The proposed analog & digital opportunistic feedback schemes are
shown to achieve the same sum-rate throughput as that achieved by dedicated
feedback schemes, but with feedback channels growing only logarithmically with
number of users. Moreover, there is also a reduction in the feedback load. In
the analog feedback case, we show that the propose scheme reduces the feedback
noise which eventually results in better throughput, whereas in the digital
feedback case the proposed scheme in a noisy scenario achieves almost the
throughput obtained in a noiseless dedicated feedback scenario. We also show
that for a fixed given budget of feedback bits, there exist a trade-off between
the number of shared channels and thresholds accuracy of the feedback SINR.Comment: Submitted to IEEE Transactions on Wireless Communications, April 200
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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