5,480 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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
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