20,500 research outputs found
Exploiting Regional Differences: A Spatially Adaptive Random Access
In this paper, we discuss the potential for improvement of the simple random
access scheme by utilizing local information such as the received
signal-to-interference-plus-noise-ratio (SINR). We propose a spatially adaptive
random access (SARA) scheme in which the transmitters in the network utilize
different transmit probabilities depending on the local situation. In our
proposed scheme, the transmit probability is adaptively updated by the ratio of
the received SINR and the target SINR. We investigate the performance of the
spatially adaptive random access scheme. For the comparison, we derive an
optimal transmit probability of ALOHA random access scheme in which all
transmitters use the same transmit probability. We illustrate the performance
of the spatially adaptive random access scheme through simulations. We show
that the performance of the proposed scheme surpasses that of the optimal ALOHA
random access scheme and is comparable with the CSMA/CA scheme.Comment: 10 pages, 10 figure
Mutual Information in Frequency and its Application to Measure Cross-Frequency Coupling in Epilepsy
We define a metric, mutual information in frequency (MI-in-frequency), to
detect and quantify the statistical dependence between different frequency
components in the data, referred to as cross-frequency coupling and apply it to
electrophysiological recordings from the brain to infer cross-frequency
coupling. The current metrics used to quantify the cross-frequency coupling in
neuroscience cannot detect if two frequency components in non-Gaussian brain
recordings are statistically independent or not. Our MI-in-frequency metric,
based on Shannon's mutual information between the Cramer's representation of
stochastic processes, overcomes this shortcoming and can detect statistical
dependence in frequency between non-Gaussian signals. We then describe two
data-driven estimators of MI-in-frequency: one based on kernel density
estimation and the other based on the nearest neighbor algorithm and validate
their performance on simulated data. We then use MI-in-frequency to estimate
mutual information between two data streams that are dependent across time,
without making any parametric model assumptions. Finally, we use the MI-in-
frequency metric to investigate the cross-frequency coupling in seizure onset
zone from electrocorticographic recordings during seizures. The inferred
cross-frequency coupling characteristics are essential to optimize the spatial
and spectral parameters of electrical stimulation based treatments of epilepsy.Comment: This paper is accepted for publication in IEEE Transactions on Signal
Processing and contains 15 pages, 9 figures and 1 tabl
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