Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach

Abstract

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.

References in corpus (3)

• Distributed Wireless Power Transfer with Energy Feedback
• Optimum Transmission Policies for Energy Harvesting Sensor Networks Powered By a Mobile Control Center
• Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model

Cited by in corpus (5)

• Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
• Collaborative Artificial Intelligence (AI) for User-Cell association in Ultra-Dense Cellular Systems
• Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks
• Mean Field Equilibrium in Multi-Armed Bandit Game with Continuous Reward
• Cognitive Hierarchy Theory for Distributed Resource Allocation in the Internet of Things