Journal ArticleDOI
Reinforcement learning for joint radio resource management in LTE-UMTS scenarios
TLDR
A novel dynamic JRRM algorithm for LTE-UMTS coexistence scenarios based on Reinforcement Learning (RL) is proposed, considered to be a good candidate for achieving the desired degree of flexibility and adaptability in future reconfigurable networks.About:
This article is published in Computer Networks.The article was published on 2011-05-01. It has received 22 citations till now. The article focuses on the topics: Radio resource management & UMTS frequency bands.read more
Citations
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Journal ArticleDOI
Recent Advances in Radio Resource Management for Heterogeneous LTE/LTE-A Networks
TL;DR: This paper presents a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes.
Proceedings ArticleDOI
Learning-based coexistence for LTE operation in unlicensed bands
TL;DR: A distributed Q-learning mechanism that exploits prior experience is proposed to support channel selection functionality for LTE-U enabled cells to decide the most appropriate channel to use for downlink traffic offloading in the unlicensed band, thus enabling coexistence with other systems in a smart and efficient way.
Proceedings ArticleDOI
A Robustness Analysis of Learning-Based Coexistence Mechanisms for LTE-U Operation in Non-Stationary Conditions
TL;DR: A Q-learning based Channel Selection strategy to decide the most appropriate channel to use for downlink traffic offloading in the unlicensed band as a mechanism to greatly facilitate the coexistence among several LTE-U and/or Wi-Fi systems in the same band is considered.
Proceedings ArticleDOI
On modeling channel selection in LTE-U as a repeated game
TL;DR: A fully distributed approach where each small cell autonomously selects the channel to set-up an LTE-U carrier is considered, using the Iterative Trial and Error Learning - Best Action learning algorithm to drive convergence towards a Nash Equilibrium.
Journal ArticleDOI
A semi-Markov decision process-based joint call admission control for inter-RAT cell re-selection in next generation wireless networks
Glaucio H. S. Carvalho,Isaac Woungang,Alagan Anpalagan,Rodolfo W. L. Coutinho,João C. W. A. Costa +4 more
TL;DR: Numerical results show that the proposed optimal JCAC selects for real-time service class the biggest RAT and for non-real-timeservice class the smallest one, which follows the 3rd Generation Partnership Project (3GPP) expectations.
References
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Journal ArticleDOI
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
TL;DR: This article presents a general class of associative reinforcement learning algorithms for connectionist networks containing stochastic units that are shown to make weight adjustments in a direction that lies along the gradient of expected reinforcement in both immediate-reinforcement tasks and certain limited forms of delayed-reInforcement tasks, and they do this without explicitly computing gradient estimates.
Book
Introduction to Reinforcement Learning
TL;DR: In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.
Book
3G Evolution : HSPA and LTE for Mobile Broadband
TL;DR: In this paper, the authors present a very up-to-date and practical book, written by engineers working closely in 3GPP, gives insight into the newest technologies and standards adopted by threeGPP with detailed explanations of the specific solutions chosen and their implementation in HSPA and LTE.
Journal ArticleDOI
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
TL;DR: By defining cognitive networks, examining their relationship to other technologies, discussing critical design issues, and providing a framework for implementation, this article aims to establish a foundation for further research and discussion.
Proceedings ArticleDOI
Fairness and throughput analysis for generalized proportional fair frequency scheduling in OFDMA
TL;DR: A generalized proportional fair (GPF) scheduling algorithm is presented, which allows tweaking the trade-off between fairness and throughput performance for best effort traffic in a cellular downlink scenario.