Fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme

Pervez, Farrukh; Jaber, Mona; Qadir, Junaid; Younis, Shahzad; Imran, Muhammad Ali

doi:10.1109/iwcmc.2017.7986564

Cited by 14 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

Self Cite

View full text Add to dashboard Cite

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

show abstract

Section: ) Significant Applications Of Rl In Networkmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations

Hossain

Bečvář

2021

IEEE Access

View full text Add to dashboard Cite

Flying base stations (FlyBSs) enable ubiquitous communications in the next generation mobile networks with a flexible topology. However, a deployment of the FlyBSs intensifies interference, which can result in a degradation in the throughput of cell-edge users. In this paper, we introduce a flexible soft frequency reuse (F-SFR) that enables a self-organization of a common SFR in the networks with an unpredictable and dynamic topology with the FlyBSs. We propose a graph theory-based algorithm for an allocation of resource plans, which is understood as a bandwidth allocation and a transmission power setting in the context of SFR. Furthermore, we introduce a low-complexity implementation of the proposed resource allocation using deep neural network (DNN) to significantly reduce the computation complexity. We show that the proposed F-SFR increases the throughput of cell-edge users by 16% to 26% and, at the same time, improves the satisfaction of the cell-edge users by up to 25% compared to the state-of-the-art solutions. We also demonstrate that the proposed scheme ensures a higher fairness in the throughput among the users with respect to the state-of-the-art solutions. The implementation via DNN also outperforms all state-of-the-art solutions despite its very low complexity.

show abstract

“…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%

Passive Method for Estimating Available Throughput for Autonomous Off-Peak Data Transfer

Satoda

Takahashi

Onishi

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Large demands for mobile traffic subject base stations to frequent short-term and sharp peak loads. Recent analysis of data traffic on commercial mobile networks reported that the traffic peaks can be reduced by an average of 40% without compromising the quality of experience provided to the end user, if a peak load can be shifted for at most 20 s. To reduce peak traffic, we previously proposed a method for off-peak data transfer, with which user equipment (UE) autonomously delays receiving data, and a peak load on a base station can be shifted. In terms of off-peak transfer of data, a significant problem is determining how each UE estimates available throughput. In this paper we propose a method of passively estimating available throughput of each UE. We evaluated the effectiveness of the proposed method through experiments on experimental and commercial LTE networks. e results indicate that our method obtains more than a 0.7 correlation between actual available throughput and estimated throughput.

show abstract

Fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme

Cited by 14 publications

References 19 publications

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations

Passive Method for Estimating Available Throughput for Autonomous Off-Peak Data Transfer

Contact Info

Product

Resources

About