16,507 research outputs found
Cross-Sender Bit-Mixing Coding
Scheduling to avoid packet collisions is a long-standing challenge in
networking, and has become even trickier in wireless networks with multiple
senders and multiple receivers. In fact, researchers have proved that even {\em
perfect} scheduling can only achieve . Here
is the number of nodes in the network, and is the {\em medium
utilization rate}. Ideally, one would hope to achieve ,
while avoiding all the complexities in scheduling. To this end, this paper
proposes {\em cross-sender bit-mixing coding} ({\em BMC}), which does not rely
on scheduling. Instead, users transmit simultaneously on suitably-chosen slots,
and the amount of overlap in different user's slots is controlled via coding.
We prove that in all possible network topologies, using BMC enables us to
achieve . We also prove that the space and time
complexities of BMC encoding/decoding are all low-order polynomials.Comment: Published in the International Conference on Information Processing
in Sensor Networks (IPSN), 201
Synchronization and Noise: A Mechanism for Regularization in Neural Systems
To learn and reason in the presence of uncertainty, the brain must be capable
of imposing some form of regularization. Here we suggest, through theoretical
and computational arguments, that the combination of noise with synchronization
provides a plausible mechanism for regularization in the nervous system. The
functional role of regularization is considered in a general context in which
coupled computational systems receive inputs corrupted by correlated noise.
Noise on the inputs is shown to impose regularization, and when synchronization
upstream induces time-varying correlations across noise variables, the degree
of regularization can be calibrated over time. The proposed mechanism is
explored first in the context of a simple associative learning problem, and
then in the context of a hierarchical sensory coding task. The resulting
qualitative behavior coincides with experimental data from visual cortex.Comment: 32 pages, 7 figures. under revie
The Practical Challenges of Interference Alignment
Interference alignment (IA) is a revolutionary wireless transmission strategy
that reduces the impact of interference. The idea of interference alignment is
to coordinate multiple transmitters so that their mutual interference aligns at
the receivers, facilitating simple interference cancellation techniques. Since
IA's inception, researchers have investigated its performance and proposed
improvements, verifying IA's ability to achieve the maximum degrees of freedom
(an approximation of sum capacity) in a variety of settings, developing
algorithms for determining alignment solutions, and generalizing transmission
strategies that relax the need for perfect alignment but yield better
performance. This article provides an overview of the concept of interference
alignment as well as an assessment of practical issues including performance in
realistic propagation environments, the role of channel state information at
the transmitter, and the practicality of interference alignment in large
networks.Comment: submitted to IEEE Wireless Communications Magazin
- …