Human-Behavior and QoE-Aware Dynamic Channel Allocation for 5G Networks: A Latent Contextual Bandit Learning Approach
Zhou, P; Xu, J; Wang, W; et al.Jiang, C; Wang, K; Hu, J
Date: 28 January 2020
Journal
IEEE Transactions on Cognitive Communications and Networking
Publisher
Institute of Electrical and Electronics Engineers
Publisher DOI
Abstract
With the rapid advance of smart wireless technologies,
a plethora of human behavioral data are generated in
5G networks, which is reported capable to improve network
performance by leveraging intelligent channel resource allocation
through big data analytics. However, what information can be
extracted for the network mobility ...
With the rapid advance of smart wireless technologies,
a plethora of human behavioral data are generated in
5G networks, which is reported capable to improve network
performance by leveraging intelligent channel resource allocation
through big data analytics. However, what information can be
extracted for the network mobility management, how to exploit
the knowledge for resource allocation and to meet the usercentric
quality of experience (QoE) are not well understood
and fully explored. To address this problem, we propose an
online learning algorithm for dynamic channel allocation based
on contextual multi-armed bandit (CMAB) theory. Especially, we
divide the stochastic human behavioral data into two categories:
the user location and the QoE-driven context. Noticing that
the distributions of CSI vary spatially, we define a set of
user’s geographic locations that shares the same set of CSI
distributions as a cluster, and the stochastic channel distributions
vary across clusters. The problem is formulated as a novel
latent SCB problem, where the proposed agnostic SCB algorithm
could automatically find the underlying clusters and significantly
improve the learning performance. We then extend our online
learning algorithm into the practical multi-user random access
scenario.We conduct experiments on a real dataset collected from
China Mobile, which indicate that our algorithms outperform
existing approaches tremendously and perform extremely well in
large-scale and high-mobility networks.
Computer Science
Faculty of Environment, Science and Economy
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