11 research outputs found
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
Fundamentals of Heterogeneous Cellular Networks with Energy Harvesting
We develop a new tractable model for K-tier heterogeneous cellular networks
(HetNets), where each base station (BS) is powered solely by a self-contained
energy harvesting module. The BSs across tiers differ in terms of the energy
harvesting rate, energy storage capacity, transmit power and deployment
density. Since a BS may not always have enough energy, it may need to be kept
OFF and allowed to recharge while nearby users are served by neighboring BSs
that are ON. We show that the fraction of time a k^{th} tier BS can be kept ON,
termed availability \rho_k, is a fundamental metric of interest. Using tools
from random walk theory, fixed point analysis and stochastic geometry, we
characterize the set of K-tuples (\rho_1, \rho_2, ... \rho_K), termed the
availability region, that is achievable by general uncoordinated operational
strategies, where the decision to toggle the current ON/OFF state of a BS is
taken independently of the other BSs. If the availability vector corresponding
to the optimal system performance, e.g., in terms of rate, lies in this
availability region, there is no performance loss due to the presence of
unreliable energy sources. As a part of our analysis, we model the temporal
dynamics of the energy level at each BS as a birth-death process, derive the
energy utilization rate, and use hitting/stopping time analysis to prove that
there exists a fundamental limit on \rho_k that cannot be surpassed by any
uncoordinated strategy.Comment: submitted to IEEE Transactions on Wireless Communications, July 201