A Contextual Bandit Approach to the Interface Selection Problem

Nikolov, Georgi; Kühn, Michael; Wenning, Bernd-Ludwig

doi:10.1109/etfa.2019.8869285

Cited by 4 publications

(10 citation statements)

References 14 publications

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“…It relies on a random variable which determines whether exploration or exploitation should be performed. In previous work [1] it was demonstrated that such algorithms are inferior to context-based approaches.…”

Section: Related Work a Interface Selection Approachesmentioning

confidence: 99%

“…In [1], this mechanism was extended further, resulting in the Modified LinUCB (ModLinUCB) approach. In Mod-LinUCB, the applicability of the LinUCB approach to the interface selection problem was shown, where the arms are modelling two mobile channels.…”

Section: Modlinucbmentioning

confidence: 99%

“…Although the ModLinUCB policy is able to react to unexpected network congestion and outperforms "follow the expert" policies in such scenarios [1], it suffers from one substantial drawback: If there is an underlying relationship between the channel quality parameters and the data rate, the ModLinUCB would not be able to take advantage of it. The way the algorithm is building its confidence about its prediction of interface i depends on the number of samples the algorithm has seen from this interface.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The main objective of the agent is to maximize its reward. Prior work focused on designing an agent that has the ability to select an interface based on regression predictions [1]. The policy is based on the Linear Upper Confidence Bound (LinUCB) algorithm first considered in [2].…”

Section: Introductionmentioning

confidence: 99%

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MABASR—A Robust Wireless Interface Selection Policy for Heterogeneous Vehicular Networks

et al. 2022

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Section: Related Work a Interface Selection Approachesmentioning

confidence: 99%

Section: Modlinucbmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MABASR—A Robust Wireless Interface Selection Policy for Heterogeneous Vehicular Networks

et al. 2022

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“…LinUCB is used in [216] for solving RAT selection in intelligent transportation systems (ITSs). The scenario considers a train, embedded with sensors collecting and sending data to a gateway.…”

Section: ) Stateless Mabmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.

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AI-Enhanced Connection Management for Cellular Networks

Wenning

2024

Studies in Computational Intelligence

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Cellular networks such as 4G, 5G and beyond are essential components of current and future communication infrastructure. Current research topics such as smart mobility, intelligent transportation systems and autonomous driving heavily depend on the availability of ubiquitous connectivity. In addition, consumer demand for cellular network resources is ever growing. In the context of the InSecTT project, cellular connectivity contributes, e.g., to use cases in the rail domain, where remote monitoring, maintenance and diagnostics rely on the presence of cellular uplinks from the rolling stock to cloud services. As the rolling stock moves, these uplinks are subject to continuously changing conditions that affect the availability and quality of the connection. To counteract this and to increase resilience, onboard systems may be equipped with a number of uplink options that can be used. This chapter discusses the use of AI to assess and manage the available connections and to move data traffic between the available links according to their expected data rates.

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A Contextual Bandit Approach to the Interface Selection Problem

Cited by 4 publications

References 14 publications

MABASR—A Robust Wireless Interface Selection Policy for Heterogeneous Vehicular Networks

MABASR—A Robust Wireless Interface Selection Policy for Heterogeneous Vehicular Networks

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

AI-Enhanced Connection Management for Cellular Networks

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