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Human-Behavior and QoE-Aware Dynamic Channel Allocation for 5G Networks: A Latent Contextual Bandit Learning Approach

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 user-centric 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.