776 research outputs found
Deep Reinforcement Learning for Resource Allocation in V2V Communications
In this article, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communication systems based on deep reinforcement
learning. Each V2V link is considered as an agent, making its own decisions to
find optimal sub-band and power level for transmission. Since the proposed
method is decentralized, the global information is not required for each agent
to make its decisions, hence the transmission overhead is small. From the
simulation results, each agent can learn how to satisfy the V2V constraints
while minimizing the interference to vehicle-to-infrastructure (V2I)
communications
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
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