11,985 research outputs found
On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time
Random Access Channel (RACH) has been identified as one of the major
bottlenecks for accommodating massive number of machine-to-machine (M2M) users
in LTE networks, especially for the case of burst arrival of connection
requests. As a consequence, the burst resolution problem has sparked a large
number of works in the area, analyzing and optimizing the average performance
of RACH. However, the understanding of what are the probabilistic performance
limits of RACH is still missing. To address this limitation, in the paper, we
investigate the reliability of RACH with access class barring (ACB). We model
RACH as a queuing system, and apply stochastic network calculus to derive
probabilistic performance bounds for burst resolution time, i.e., the worst
case time it takes to connect a burst of M2M devices to the base station. We
illustrate the accuracy of the proposed methodology and its potential
applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC),
201
Cooperative Synchronization in Wireless Networks
Synchronization is a key functionality in wireless network, enabling a wide
variety of services. We consider a Bayesian inference framework whereby network
nodes can achieve phase and skew synchronization in a fully distributed way. In
particular, under the assumption of Gaussian measurement noise, we derive two
message passing methods (belief propagation and mean field), analyze their
convergence behavior, and perform a qualitative and quantitative comparison
with a number of competing algorithms. We also show that both methods can be
applied in networks with and without master nodes. Our performance results are
complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process
Scalability is one of the major issues for real-world Vehicle-to-Vehicle
network realization. To tackle this challenge, a stochastic hybrid modeling
framework based on a non-parametric Bayesian inference method, i.e.,
hierarchical Dirichlet process (HDP), is investigated in this paper. This
framework is able to jointly model driver/vehicle behavior through forecasting
the vehicle dynamical time-series. This modeling framework could be merged with
the notion of model-based information networking, which is recently proposed in
the vehicular literature, to overcome the scalability challenges in dense
vehicular networks via broadcasting the behavioral models instead of raw
information dissemination. This modeling approach has been applied on several
scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data
set and the results show a higher performance of this model in comparison with
the zero-hold method as the baseline.Comment: This is the accepted version of the paper in 2018 IEEE 88th Vehicular
Technology Conference (VTC2018-Fall) (references added, title and abstract
modified
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