5,894 research outputs found
Full-duplex MAC Protocol Design and Analysis
The idea of in-band full-duplex (FD) communications revives in recent years
owing to the significant progress in the self-interference cancellation and
hardware design techniques, offering the potential to double spectral
efficiency. The adaptations in upper layers are highly demanded in the design
of FD communication systems. In this letter, we propose a novel medium access
control (MAC) using FD techniques that allows transmitters to monitor the
channel usage while transmitting, and backoff as soon as collision happens.
Analytical saturation throughput of the FD-MAC protocol is derived with the
consideration of imperfect sensing brought by residual self- interference (RSI)
in the PHY layer. Both analytical and simulation results indicate that the
normalized saturation throughput of the proposed FD-MAC can significantly
outperforms conventional CSMA/CA under various network conditions
Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing
Given significant air pollution problems, air quality index (AQI) monitoring
has recently received increasing attention. In this paper, we design a mobile
AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on basis of the neural network (GPM-NN), to
physically characterize the particle dispersion in the air. Based on GPM-NN, we
propose a battery efficient and adaptive monitoring algorithm to monitor AQI at
the selected locations and construct an accurate AQI map with the sensed data.
The proposed adaptive monitoring algorithm is evaluated in two typical
scenarios, a two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. Experimental
results demonstrate that our system can provide higher prediction accuracy of
AQI with GPM-NN than other existing models, while greatly reducing the power
consumption with the adaptive monitoring algorithm
Federated Empirical Risk Minimization via Second-Order Method
Many convex optimization problems with important applications in machine
learning are formulated as empirical risk minimization (ERM). There are several
examples: linear and logistic regression, LASSO, kernel regression, quantile
regression, -norm regression, support vector machines (SVM), and mean-field
variational inference. To improve data privacy, federated learning is proposed
in machine learning as a framework for training deep learning models on the
network edge without sharing data between participating nodes. In this work, we
present an interior point method (IPM) to solve a general ERM problem under the
federated learning setting. We show that the communication complexity of each
iteration of our IPM is , where is the dimension (i.e.,
number of features) of the dataset
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