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Artificial Intelligence Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach

Zijun Han, Tao Lei, Zhaoming Lu, Xiangming Wen, Wei Zheng, Lingchao Guo
2019 IEEE Access  
Along with it, we propose DCRQN, a novel handoff management scheme based on deep reinforcement learning, specifically deep Q-network.  ...  INDEX TERMS Deep reinforcement learning, handoff, SDN, WLAN.  ...  CONTRIBUTIONS In the paper, we propose DCRQN, a DQN-based handoff management scheme for dense WLANs.  ... 
doi:10.1109/access.2019.2900445 fatcat:q2lxkhserzhevn2sny4opafxjq

Proactive Cross-Layer Framework Based on Classification Techniques for Handover Decision on WLAN Environments

Josué Vicente Cervantes-Bazán, Alma Delia Cuevas-Rasgado, Luis Martín Rojas-Cárdenas, Saúl Lazcano-Salas, Farid García-Lamont, Luis Arturo Soriano, José de Jesús Rubio, Jaime Pacheco
2022 Electronics  
In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments.  ...  The proposed framework considers a decision-making process based on five classifiers and the user's position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy  ...  Authorsthankthe Instituto Politécnico Nacional, Secretaría de Investigación y Posgrado, Comisión de Operación y Fomento de Actividades Académicas, and Consejo Nacional de Ciencia y Tecnología for their  ... 
doi:10.3390/electronics11050712 fatcat:cfa7kwbfuzdk3fsncac67arfkq

Distributed and Collaborative High Speed Inference Deep Learning for Mobile Edge with Topological Dependencies

Shagufta Henna, Alan Davy
2020 IEEE Transactions on Cloud Computing  
Index Terms-deep learning in edge computing, deep learning in cloud computing, edge inference, edge with topological dependencies, intelligent edge, intelligent cloud computing.  ...  To bring more intelligence to the edge under topological dependencies, compared to optimization heuristics, this work proposes a novel collaborative distributed DL approach.  ...  handoff management.  ... 
doi:10.1109/tcc.2020.2978846 fatcat:aw2lsygdpzg77fwdtivdhjwopq

Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

Guofeng Zhao, Yong Li, Chuan Xu, Zhenzhen Han, Yuan Xing, Shui Yu
2019 IEEE Access  
In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively.  ...  Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy  ...  In [39] , the author propose a handoff management scheme based on deep RL in WLANs, which can effectively improve the data rate.  ... 
doi:10.1109/access.2019.2937438 fatcat:dkognijvlnf33nhchvnyknttee

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks [article]

Jingjing Wang and Chunxiao Jiang and Haijun Zhang and Yong Ren and Kwang-Cheng Chen and Lajos Hanzo
2020 arXiv   pre-print
Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning.  ...  Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services  ...  In [345] , a deep reinforcement learning aided communication-based train control system was conceived by Zhu et al. which jointly optimized the communication handoff strategy and the control functions  ... 
arXiv:1902.01946v2 fatcat:7bveg6rmjfga5mftdkr3mst2qa

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning [article]

Szymon Szott, Katarzyna Kosek-Szott, Piotr Gawłowicz, Jorge Torres Gómez, Boris Bellalta, Anatolij Zubow, Falko Dressler
2022 arXiv   pre-print
While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity.  ...  In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied.  ...  [188] describe a handover management scheme for dense WLAN networks, which uses DRL, specifically a deep Q-network.  ... 
arXiv:2109.04786v3 fatcat:ny55qfhsnfduzcxyve5mylpr2m

Comprehensive Survey of Machine Learning Approaches in Cognitive Radio-based Vehicular Ad Hoc Networks

Mohammad Asif Hossain, Rafidah Md Noor, Kok-Lim Alvin Yau, Saaidal Razalli Azzuhri, Muhammad Reza Zraba, Ismail Ahmedy
2020 IEEE Access  
Similar to a robot, an autonomous vehicle (AV) can learn the surrounding environment and communicate with increased safety, reliability, QoS, and energy efficiency by applying such learning.  ...  To ensure this role, a gigantic amount of data should be exchanged. However, current allocated wireless access for VANET is inadequate to handle such massive data amounts.  ...  ACKNOWLEDGEMENT The authors would like to thank the anonymous reviewers for their comments and constructive suggestions which helped them to improve this manuscript.  ... 
doi:10.1109/access.2020.2989870 fatcat:fpp3m3rts5gghcnlkyelzt3ykq

Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks - A Survey

2021 KSII Transactions on Internet and Information Systems  
On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR).  ...  Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems.  ...  Several works focused on reinforcement learning or deep reinforcement learning based spectrum sensing [176] .  ... 
doi:10.3837/tiis.2021.07.020 fatcat:uzb3gqtsezcgzehvjige3vgiwq

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey [article]

Nguyen Cong Luong, Dinh Thai Hoang, Shimin Gong, Dusit Niyato, Ping Wang, Ying-Chang Liang, Dong In Kim
2018 arXiv   pre-print
Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings.  ...  Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking.  ...  INTRODUCTION Reinforcement learning [1] is one of the most important research directions of machine learning which has significant impacts to the development of Artificial Intelligence (AI) over the  ... 
arXiv:1810.07862v1 fatcat:qc3mqk2norazvc2xnynau6bqzu

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

Hongtao Zhang, Lingcheng Dai
2019 IEEE Access  
Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds  ...  Moreover, the learning perspective of solutions to mobility prediction has been studied.  ...  More like [140] , optimal handover controllers in wireless systems were learned by a deep neural network and reinforcement learning, and controlled the handover process via predicting user's pattern that  ... 
doi:10.1109/access.2018.2885821 fatcat:uz74ihsawbgw3g4xnkt4m5bvoe

2020 Index IEEE Systems Journal Vol. 14

2020 IEEE Systems Journal  
Mukherjee, A., +, JSYST March 2020 353-362 Games Intelligent Residential Energy Management System Using Deep Reinforcement Learning.  ...  ., +, JSYST Sept. 2020 4262-4271 Load modeling Intelligent Residential Energy Management System Using Deep Reinforce- ment Learning.  ...  : An Emergent Intelligence Technique.  ... 
doi:10.1109/jsyst.2021.3054547 fatcat:zf2aafvnfzbeje32qei5563myu

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey [article]

Xiaofei Wang and Yiwen Han and Victor C.M. Leung and Dusit Niyato and Xueqiang Yan and Xu Chen
2019 arXiv   pre-print
As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications  ...  In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management  ...  MDC Micro Data Center VNF Virtual Network Function DQL Deep Q-Learning MDP Markov Decision Process V2V Vehicle-to-Vehicle DRL Deep Reinforcement Learning MLP Multi-Layer Perceptron WLAN  ... 
arXiv:1907.08349v2 fatcat:4hfqgdto4fhvlguwfjxuz3ik5q

When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges [article]

Qun Wang, Haijian Sun, Rose Qingyang Hu, Arupjyoti Bhuyan
2022 arXiv   pre-print
Machine learning (ML) based methods have frequently been proposed to address those issues.  ...  In this article, we provide a comprehensive survey of the recent development of ML based SS methods, the most critical security issues, and corresponding defense mechanisms.  ...  A Transfer Actor-Critic Learning (TACT) algorithm was proposed for spectrum handoff management in [41] .  ... 
arXiv:2201.04677v1 fatcat:gb73bku37fatncamax67buwkea

Bridging Socially Enhanced Virtual Communities

Venugopal Rao
2013 IOSR Journal of Computer Engineering  
It is achieved by discovering present relevant businesses and prospective business alliances by developing a semi-automated approach.  ...  The empirical results revealed that the proposed system is effective when tested with test bed which is based on distributed technology such as web services.  ...  help given to us in the completion of our project titled, 'Video Watermarking scheme based on DWT and PCA for copyright protection'.  ... 
doi:10.9790/0661-0940104 fatcat:zejgpenvqzb7rkchkznv5igfwm

Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives [article]

Yassine Maalej, Elyes Balti
2022 arXiv   pre-print
vision and Artificial Intelligence (AI) techniques.  ...  modelling, medium access, VC Computing (VCC), VC collation strategies, security issues, and autonomous driving (AD) including 3D environment learning approaches and AD enabling deep-learning, computer  ...  On the other hand, many deep learning and artificial intelligence systems and algorithms have been developed and tailored to enhance autonomous vehicles' 3D surrounding reconstruction and image flow recognition  ... 
arXiv:2201.02893v2 fatcat:bqnscosmqrfdvihexa4mhqv2dy
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