Journal ArticleDOI
Self-Organization in Small Cell Networks: A Reinforcement Learning Approach
TLDR
Numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration/exploitation, myopic/foresighted behavior and complete/incomplete information.Abstract:
In this paper, a decentralized and self-organizing mechanism for small cell networks (such as micro-, femto- and picocells) is proposed. In particular, an application to the case in which small cell networks aim to mitigate the interference caused to the macrocell network, while maximizing their own spectral efficiencies, is presented. The proposed mechanism is based on new notions of reinforcement learning (RL) through which small cells jointly estimate their time-average performance and optimize their probability distributions with which they judiciously choose their transmit configurations. Here, a minimum signal to interference plus noise ratio (SINR) is guaranteed at the macrocell user equipment (UE), while the small cells maximize their individual performances. The proposed RL procedure is fully distributed as every small cell base station requires only an observation of its instantaneous performance which can be obtained from its UE. Furthermore, it is shown that the proposed mechanism always converges to an epsilon Nash equilibrium when all small cells share the same interest. In addition, this mechanism is shown to possess better convergence properties and incur less overhead than existing techniques such as best response dynamics, fictitious play or classical RL. Finally, numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration/exploitation, myopic/foresighted behavior and complete/incomplete information.read more
Citations
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Journal ArticleDOI
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale
TL;DR: In this article, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture and topology (across access, edge, and core), and decision-making under uncertainty is provided.
Journal ArticleDOI
Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience
TL;DR: In this article, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality of experience (QoE) of wireless devices in a cloud radio access network is studied.
Journal ArticleDOI
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
TL;DR: In this paper, the authors present a survey of the recent advances of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in network layer, and localization in the application layer.
Journal ArticleDOI
Virtual Reality Over Wireless Networks: Quality-of-Service Model and Learning-Based Resource Management
TL;DR: In this article, the problem of resource management for a network of wireless virtual reality (VR) users communicating over small cell networks (SCNs) is studied for the purpose of capturing the VR users' quality-of-service (QoS) in SCNs, a novel VR model, based on multi-attribute utility theory, is proposed.
Posted Content
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
TL;DR: This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resourcemanagement in the MAC layer, networking and mobility management in the network layer, and localization in the application layer.
References
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Femtocell networks: a survey
TL;DR: The technical and business arguments for femtocells are overview and the state of the art on each front is described and the technical challenges facing femtocell networks are described and some preliminary ideas for how to overcome them are given.
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Drew Fudenberg,David K. Levine +1 more
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