2 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
Scheduling Policies for Federated Learning in Wireless Networks
Motivated by the increasing computational capacity of wireless user
equipments (UEs), e.g., smart phones, tablets, or vehicles, as well as the
increasing concerns about sharing private data, a new machine learning model
has emerged, namely federated learning (FL), that allows a decoupling of data
acquisition and computation at the central unit. Unlike centralized learning
taking place in a data center, FL usually operates in a wireless edge network
where the communication medium is resource-constrained and unreliable. Due to
limited bandwidth, only a portion of UEs can be scheduled for updates at each
iteration. Due to the shared nature of the wireless medium, transmissions are
subjected to interference and are not guaranteed. The performance of FL system
in such a setting is not well understood. In this paper, an analytical model is
developed to characterize the performance of FL in wireless networks.
Particularly, tractable expressions are derived for the convergence rate of FL
in a wireless setting, accounting for effects from both scheduling schemes and
inter-cell interference. Using the developed analysis, the effectiveness of
three different scheduling policies, i.e., random scheduling (RS), round robin
(RR), and proportional fair (PF), are compared in terms of FL convergence rate.
It is shown that running FL with PF outperforms RS and RR if the network is
operating under a high signal-to-interference-plus-noise ratio (SINR)
threshold, while RR is more preferable when the SINR threshold is low.
Moreover, the FL convergence rate decreases rapidly as the SINR threshold
increases, thus confirming the importance of compression and quantization of
the update parameters. The analysis also reveals a trade-off between the number
of scheduled UEs and subchannel bandwidth under a fixed amount of available
spectrum