2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) 2017
DOI: 10.1109/iwcmc.2017.7986564
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Fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme

Abstract: Abstract-Heterogeneous networks are a key solution to serving the exponential surge in data volume and higher quality expectations. Nonetheless, such networks require the ubiquitous presence of fiberto-the-cell to address the performance demands of 5G and fastspreading small cells. To this end, innovative ways of optimizing the usage of realistic backhaul links are being investigated. In this work, we propose a fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme.The proposed scheme … Show more

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Cited by 14 publications
(6 citation statements)
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“…RL has also been proposed as an aid for enhancing security schemes for CRNs through the detection of malicious nodes and their attacks they launch [165]. Q-learning is another popular RL technique that has been applied in the networking context-e.g., we highlight one example application of Q-learning in the context of Heterogeneous Mobile Networks (HetNets) [166] in which the authors proposed a fuzzy Q-learning based user-centric cell association scheme for ensuring appropriate QoS provisioning for users with results improving the state of the art.…”
Section: ) Significant Applications Of Rl In Networkmentioning
confidence: 99%
“…RL has also been proposed as an aid for enhancing security schemes for CRNs through the detection of malicious nodes and their attacks they launch [165]. Q-learning is another popular RL technique that has been applied in the networking context-e.g., we highlight one example application of Q-learning in the context of Heterogeneous Mobile Networks (HetNets) [166] in which the authors proposed a fuzzy Q-learning based user-centric cell association scheme for ensuring appropriate QoS provisioning for users with results improving the state of the art.…”
Section: ) Significant Applications Of Rl In Networkmentioning
confidence: 99%
“…In [31], [32], Q-learning is exploited for the transmission power control to improve the throughput by reducing interference in the heterogeneous networks. A similar approach is adopted for the association of the users in [33], [34]. Machine learning, by means of support vector machine, is adopted also in [35], [36] for the power control in cognitive radio networks.…”
Section: Related Workmentioning
confidence: 99%
“…A simple model that regresses LTE download throughput on independent metrics, i.e., the reference signal receive power (RSRP), reference signal receive quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR), was developed [16,17]. However, while the correlation with each parameter can be checked, it does not show how to accurately predict the throughput.…”
Section: Related Workmentioning
confidence: 99%