5,584 research outputs found
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled
either by a centralized scheduler residing in the network (e.g., a base station
in case of cellular systems) or a distributed scheduler, where the resources
are autonomously selected by the vehicles. The former approach yields a
considerably higher resource utilization in case the network coverage is
uninterrupted. However, in case of intermittent or out-of-coverage, due to not
having input from centralized scheduler, vehicles need to revert to distributed
scheduling. Motivated by recent advances in reinforcement learning (RL), we
investigate whether a centralized learning scheduler can be taught to
efficiently pre-assign the resources to vehicles for out-of-coverage V2V
communication. Specifically, we use the actor-critic RL algorithm to train the
centralized scheduler to provide non-interfering resources to vehicles before
they enter the out-of-coverage area. Our initial results show that a RL-based
scheduler can achieve performance as good as or better than the state-of-art
distributed scheduler, often outperforming it. Furthermore, the learning
process completes within a reasonable time (ranging from a few hundred to a few
thousand epochs), thus making the RL-based scheduler a promising solution for
V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201
Leading Undergraduate Students to Big Data Generation
People are facing a flood of data today. Data are being collected at
unprecedented scale in many areas, such as networking, image processing,
virtualization, scientific computation, and algorithms. The huge data nowadays
are called Big Data. Big data is an all encompassing term for any collection of
data sets so large and complex that it becomes difficult to process them using
traditional data processing applications. In this article, the authors present
a unique way which uses network simulator and tools of image processing to
train students abilities to learn, analyze, manipulate, and apply Big Data.
Thus they develop students handson abilities on Big Data and their critical
thinking abilities. The authors used novel image based rendering algorithm with
user intervention to generate realistic 3D virtual world. The learning outcomes
are significant
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