Learning-based resource partitioning in heterogeneous networks with multiple network operatorsLearning-based resource partitioning in heterogeneous networks with multiple network operators

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In heterogeneous network, it is important to mitigate cross-tier interference. Resource partitioning is the one of solution to reduce interference. However, most of studies on partitioning assumed that there was only one network operator to cooperate with. In this letter, we study the network selection problem of small access points in heterogeneous networks, which are provided by multiple network operators. We model the multiple network operators scenario as a congestion game. To solve the equilibrium point of suggested game, we analyze some features of proposed model such as potential game property, smoothed best response dynamics and logit equilibrium. Then, we propose a reinforcement learning algorithm that can reach logit equilibrium in distributed way. Moreover, we also suggest the adjustment of learning parameters to enhance adaptability. By means of simulations, it is shown that proposed algorithm has near-optimal performance in view of throughput, fairness and adaptability. © 1997-2012 IEEE.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-03
Language
English
Article Type
Article
Citation

IEEE COMMUNICATIONS LETTERS, v.25, no.3, pp.869 - 873

ISSN
1089-7798
DOI
10.1109/LCOMM.2020.3037232
URI
http://hdl.handle.net/10203/282317
Appears in Collection
EE-Journal Papers(저널논문)
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