Multi-armed Bandits with Application to 5G Small Cells

Abstract

Due to the pervasive demand for mobile services, next generation wireless networks are expected to be able to deliver high date rates while wireless resources become more and more scarce. This requires the next generation wireless networks to move towards new networking paradigms that are able to efficiently support resource-demanding applications such as personalized mobile services. Examples of such paradigms foreseen for the emerging fifth generation (5G) cellular networks include very densely deployed small cells and device-to-device communications. For 5G networks, it will be imperative to search for spectrum and energy-efficient solutions to the resource allocation problems that i) are amenable to distributed implementation, ii) are capable of dealing with uncertainty and lack of information, and iii) can cope with users' selfishness. The core objective of this article is to investigate and to establish the potential of multi-armed bandit (MAB) framework to address this challenge. In particular, we provide a brief tutorial on bandit problems, including different variations and solution approaches. Furthermore, we discuss recent applications as well as future research directions. In addition, we provide a detailed example of using an MAB model for energy-efficient small cell planning in 5G networks.

10 pages

References in corpus (2)

• Evolution Towards 5G Multi-tier Cellular Wireless Networks: An Interference Management Perspective
• Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

Cited by in corpus (13)

• Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
• Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks
• Distributed Cell Association for Energy Harvesting IoT Devices in Dense Small Cell Networks: A Mean-Field Multi-Armed Bandit Approach
• Distributed User Association in Energy Harvesting Small Cell Networks: A Probabilistic Model
• Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks with Uncertainty
• Task Offloading for Large-Scale Asynchronous Mobile Edge Computing: An Index Policy Approach
• Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits
• Stochastic Bandits with Linear Constraints
• Multi-Player Multi-Armed Bandit Based Resource Allocation for D2D Communications
• Distributed Task Management in Fog Computing: A Socially Concave Bandit Game
• Interference Mitigation and Resource Allocation in Underlay Cognitive Radio Networks
• A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks
• Adversarial Linear Contextual Bandits with Graph-Structured Side Observations