# Multi-armed Bandits with Application to 5G Small Cells

arXiv: https://arxiv.org/abs/1510.00627 · HTML: https://mlsanity.com/paper/1510.00627/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2015.

## Built on by (most cited first, 18 of 18)

- [Linear Combinatorial Semi-Bandit with Causally Related Rewards](https://mlsanity.com/paper/2212.12923/) (2022)
- [Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing](https://mlsanity.com/paper/2302.05257/) (2023)
- [Variational Quantum Optimization with Continuous Bandits](https://mlsanity.com/paper/2502.04021/) (2025)
- [Distributed Task Management in Fog Computing: A Socially Concave Bandit Game](https://mlsanity.com/paper/2203.14572/) (2022)
- [Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards](https://mlsanity.com/paper/2307.09093/) (2023)
- [Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards](https://mlsanity.com/paper/2307.14138/) (2023)
- [Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization](https://mlsanity.com/paper/2308.16889/) (2023)
- [Energy-Efficient Sleep Mode Optimization of 5G mmWave Networks Using Deep Contextual MAB](https://mlsanity.com/paper/2405.09528/) (2024)
- [Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality](https://mlsanity.com/paper/2510.12152/) (2025)
- [A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks](https://mlsanity.com/paper/2105.05564/) (2021)
- [Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems](https://mlsanity.com/paper/2202.03167/) (2022)
- [Exploiting Side Information for Improved Online Learning Algorithms in Wireless Networks](https://mlsanity.com/paper/2202.11699/) (2022)
- [Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation](https://mlsanity.com/paper/2212.09926/) (2022)
- [Cooperative Thresholded Lasso for Sparse Linear Bandit](https://mlsanity.com/paper/2305.19161/) (2023)
- [Online Learning with Costly Features in Non-stationary Environments](https://mlsanity.com/paper/2307.09388/) (2023)
- [Maximal-Capacity Discrete Memoryless Channel Identification](https://mlsanity.com/paper/2401.10204/) (2024)
- [Multi-armed Bandits with Missing Outcome](https://mlsanity.com/paper/2411.05661/) (2024)
- [Energy Efficient Sleep Mode Optimization in 5G mmWave Networks via Multi Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2511.22105/) (2025)

## Abstract (arXiv)

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.