9,869 research outputs found
Demystifying the Scaling Laws of Dense Wireless Networks: No Linear Scaling in Practice
We optimize the hierarchical cooperation protocol of Ozgur, Leveque and Tse,
which is supposed to yield almost linear scaling of the capacity of a dense
wireless network with the number of users . Exploiting recent results on the
optimality of "treating interference as noise" in Gaussian interference
channels, we are able to optimize the achievable average per-link rate and not
just its scaling law. Our optimized hierarchical cooperation protocol
significantly outperforms the originally proposed scheme. On the negative side,
we show that even for very large , the rate scaling is far from linear, and
the optimal number of stages is less than 4, instead of as required for almost linear scaling. Combining our results and the
fact that, beyond a certain user density, the network capacity is fundamentally
limited by Maxwell laws, as shown by Francheschetti, Migliore and Minero, we
argue that there is indeed no intermediate regime of linear scaling for dense
networks in practice.Comment: 5 pages, 6 figures, ISIT 2014. arXiv admin note: substantial text
overlap with arXiv:1402.181
A pairwise maximum entropy model describes energy landscape for spiral wave dynamics of cardiac fibrillation
Heart is an electrically-connected network. Spiral wave dynamics of cardiac
fibrillation shows chaotic and disintegrated patterns while sinus rhythm shows
synchronized excitation patterns. To determine functional interactions between
cardiomyocytes during complex fibrillation states, we applied a pairwise
maximum entropy model (MEM) to the sequential electrical activity maps acquired
from the 2D computational simulation of human atrial fibrillation. Then, we
constructed energy landscape and estimated hierarchical structure among the
different local minima (attractors) to explain the dynamic properties of
cardiac fibrillation. Four types of the wave dynamics were considered: sinus
rhythm; single stable rotor; single rotor with wavebreak; and multiple wavelet.
The MEM could describe all types of wave dynamics (both accuracy and
reliability>0.9) except the multiple random wavelet. Both of the sinus rhythm
and the single stable rotor showed relatively high pairwise interaction
coefficients among the cardiomyocytes. Also, the local energy minima had
relatively large basins and high energy barrier, showing stable attractor
properties. However, in the single rotor with wavebreak, there were relatively
low pairwise interaction coefficients and a similar number of the local minima
separated by a relatively low energy barrier compared with the single stable
rotor case. The energy landscape of the multiple wavelet consisted of a large
number of the local minima separated by a relatively low energy barrier,
showing unstable dynamics. These results indicate that the MEM provides
information about local and global coherence among the cardiomyocytes beyond
the simple structural connectivity. Energy landscape analysis can explain
stability and transitional properties of complex dynamics of cardiac
fibrillation, which might be determined by the presence of 'driver' such as
sinus node or rotor.Comment: Presented at the 62nd Biophysical Society Annual Meeting, San
Francisco, California, 201
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