The emerging Internet of Things (IoT)-driven ultra-dense small cell networks
(UD-SCNs) will need to combat a variety of challenges. On one hand, massive
number of devices sharing the limited wireless resources will render
centralized control mechanisms infeasible due to the excessive cost of
information acquisition and computations. On the other hand, to reduce energy
consumption from fixed power grid and/or battery, many IoT devices may need to
depend on the energy harvested from the ambient environment (e.g., from RF
transmissions, environmental sources). However, due to the opportunistic nature
of energy harvesting, this will introduce uncertainty in the network operation.
In this article, we study the distributed cell association problem for energy
harvesting IoT devices in UD-SCNs. After reviewing the state-of-the-art
research on the cell association problem in small cell networks, we outline the
major challenges for distributed cell association in IoT-driven UD-SCNs where
the IoT devices will need to perform cell association in a distributed manner
in presence of uncertainty (e.g., limited knowledge on channel/network) and
limited computational capabilities. To this end, we propose an approach based
on mean-field multi-armed bandit games to solve the uplink cell association
problem for energy harvesting IoT devices in a UD-SCN. This approach is
particularly suitable to analyze large multi-agent systems under uncertainty
and lack of information. We provide some theoretical results as well as
preliminary performance evaluation results for the proposed approach.

本文提出了一种基于均值场多臂老虎机博弈方法的能量收集 Internet of Things 小型网络（UD-SCN）上行小区关联问题的分布式解决方案，并解决了面临的不确定性和信息不足的大型多智能体系统分析问题。