5,449 research outputs found
A Unified Framework for SINR Analysis in Poisson Networks with Traffic Dynamics
We study the performance of wireless links for a class of Poisson networks,
in which packets arrive at the transmitters following Bernoulli processes. By
combining stochastic geometry with queueing theory, two fundamental measures
are analyzed, namely the transmission success probability and the meta
distribution of signal-to-interference-plus-noise ratio (SINR). Different from
the conventional approaches that assume independent active states across the
nodes and use homogeneous point processes to model the locations of
interferers, our analysis accounts for the interdependency amongst active
states of the transmitters in space and arrives at a non-homogeneous point
process for the modeling of interferers' positions, which leads to a more
accurate characterization of the SINR. The accuracy of the theoretical results
is verified by simulations, and the developed framework is then used to devise
design guidelines for the deployment strategies of wireless networks
Particle Motion in a Liquid Film Rimming the Inside of a Partially Filled Rotating Cylinder
Both lighter- and hydrophobic heavier-than-liquid particles will float on liquid–air surfaces. Capillary forces cause the particles to cluster in typical situations identified here. This kind of clustering causes particles to segregate into islands and bands of high concentrations in thin liquid films rimming the inside of a slowly rotating cylinder partially filled with liquid. A second regime of particle segregation, driven by secondary motions induced by off-centre gas bubbles in a more rapidly rotating cylinder at higher filling levels, is identified. A third regime of segregation of bidisperse suspensions is found in which two layers of heavier-than-liquid particles that stratify when there is no rotation, segregate into alternate bands of particles when there is rotation
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
Federated edge learning is envisioned as the bedrock of enabling intelligence
in next-generation wireless networks, but the limited spectral resources often
constrain its scalability. In light of this challenge, a line of recent
research suggested integrating analog over-the-air computations into federated
edge learning systems, to exploit the superposition property of electromagnetic
waves for fast aggregation of intermediate parameters and achieve (almost)
unlimited scalability. Over-the-air computations also benefit the system in
other aspects, such as low hardware cost, reduced access latency, and enhanced
privacy protection. Despite these advantages, the interference introduced by
wireless communications also influences various aspects of the model training
process, while its importance is not well recognized yet. This article provides
a comprehensive overview of the positive and negative effects of interference
on over-the-air computation-based edge learning systems. The potential open
issues and research trends are also discussed.Comment: 7 pages, 6 figures. Accepted by IEEE Communications Magazin
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