139 Hits in 6.1 sec

Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks [article]

Apostolos Avranas
2022 arXiv   pre-print
To this purpose, we resort to deep reinforcement learning (DRL) and propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the aforementioned problem  ...  The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here.  ...  We leveraged deep reinforcement learning and proposed a deep deterministic policy gradient algorithm, which builds upon distributional reinforcement learning and deep sets.  ... 
arXiv:2011.13634v3 fatcat:quscjgsucrfihhlyiq3gttd2fe

Development of A Fully Data-Driven Artificial Intelligence and Deep Learning for URLLC Application in 6G Wireless Systems: A Survey [article]

Adeeb Salh, Lukman Audah, Qazwan Abdullah, Abdullah Noorsaliza, Nor Shahida Mohd Shah, Jameel Mukred, Shipun Hamzah
2021 arXiv   pre-print
Furthermore, improving a multi-level architecture for ultra-reliable and low latency in deep Learning allows for data-driven AI and 6G networks for device intelligence, as well as allowing innovations  ...  Data-driven for ultra-reliable and low latency communication is a new service paradigm provided by a new application of future sixth-generation wireless communication and network architecture, involving  ...  In addition, maximize the high video transmissions in the long term and intelligent schedule for packet transmission in 6G depend on enabling learning with IoT.  ... 
arXiv:2108.10076v1 fatcat:b753qbfwjrdujca6spguxobaxq

A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems

Adeeb Salh, Lukman Audah, Nor Shahida Mohd Shah, Abdulraqeb Alhammadi, Qazwan Abdullah, Yun Hee Kim, Samir A. Al-Gailani, Shipun A. Hamzah, Bashar A. F. Esmail, Akram A. Almohammedi
2021 IEEE Access  
The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture.  ...  In addition, improving a multi-level architecture for URLLC through deep learning (DL) would facilitate the creation of a data-driven AI system, 6G networks for intelligent devices, and technologies based  ...  DEEP REINFORCEMENT LEARNING Deep-RL is proposed to support model-free URLLC and a highly dynamic transmission in smart cities and reservation of reliable wireless connectivity for air networks based on  ... 
doi:10.1109/access.2021.3069707 fatcat:v5om5kdtpze3vcxm6tko3efha4

Conference Program

2020 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA)  
Learning for Network Applications 181 181 A Reinforcement Learning based Game Theoretic Approach for Distributed Power Control in Downlink NOMA Ashish Rauniyar, Anis Yazidi, Paal Engelstad and Olav  ...  With the Flow: Clustering Dynamically-Defined NetFlow Features for Network Intrusion Detection with DYNIDS Luis Dias, Simão Valente and Miguel Correia 229 Hardware performance counters-based anomaly  ... 
doi:10.1109/nca51143.2020.9306743 fatcat:2jmoxb3xrrhqpdgb2xlmqxmuee

DRL-Based Edge Computing Model to Offload the FIFA World Cup Traffic

Hongyi Li, Xinrui Che, Jianhui Lv
2020 Mobile Information Systems  
Then, the Deep Reinforcement Learning (DRL) is used to provide the traffic scheduling method and minimize the scheduling time of application programs.  ...  Meanwhile, the task scheduling operation is regarded as the process of Markov decision, and the proximal policy optimization method is used to train the Deep Neural Network in the DRL.  ...  advantages of Reinforcement Learning (RL) [19] and Deep Neural Networks (DNN) [20] and enables obtaining the optimal decision by automatically learning the network environment based on the multiple  ... 
doi:10.1155/2020/8825643 fatcat:qt5eeg6anfgudp4gr66gxlwoju

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TII July 2021 4978-4987 Deep Reinforcement Learning for Edge Service Placement in Softwarized Industrial Cyber-Physical System.  ...  ., +, TII Oct. 2021 6765-6775 Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

Table of Contents

2021 IEEE EUROCON 2021 - 19th International Conference on Smart Technologies  
Reinforcement Learning Agents for Decision Making in Blockchain Nodes Arafat Abu Mallouh, Omar Abuzaghleh and Zakariya Qawaqneh 197 70 RSO: A Novel Reinforced Swarm Optimization Algorithm for Feature  ...  Using Neural Networks with Embedding Initialized Weights Cagri Emre Yildiz, Mustafa Aker and Yusuf Yaslan 338 120 Physical-Layer Security for 5G Wireless Networks: Sharing Non-Causal CSI with the Eavesdropper  ... 
doi:10.1109/eurocon52738.2021.9535646 fatcat:l457sd2wfzevxgbn6hvlqnwyca

Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness

Oladapo Oyebode, Jonathon Fowles, Darren Steeves, Rita Orji
However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized  ...  Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration,  ...  ; Multiclass Learning Classification; negative (normal) duplicate filters; Classification (Fall, Remove Deep learning for Normalization; Fill Classification (130 Train/Test split Classification Levy Flight-based  ... 
doi:10.6084/m9.figshare.20390573.v1 fatcat:2zxzmfnawfdi3fr6zugd7zvpse

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

Ijaz Ahmad, Shariar Shahabuddin, Hassan Malik, Erkki Harjula, Teemu Leppanen, Lauri Loven, Antti Anttonen, Ali Hassan Sodhro, Muhammad Mahtab Alam, Markku Juntti, Antti Yla-Jaaski, Thilo Sauter (+3 others)
2020 IEEE Access  
For more information, see VOLUME 8, 2020 communication networks with such capabilities [19], [25] .  ...  Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services.  ...  A survey on the application of machine learning specifically supervised and unsupervised learning, reinforcement learning, Deep Neural Networks (DNNs), and transfer learning, in wireless networks is presented  ... 
doi:10.1109/access.2020.3041765 fatcat:erbcetvcrjabrl4qloow3dqcai

2019 Index IEEE Transactions on Industrial Informatics Vol. 15

2019 IEEE Transactions on Industrial Informatics  
., +, TII Jan. 2019 469-480 Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network.  ...  ., +, TII July 2019 4235-4243 Blockchain-Enabled Data Collection and Sharing for Industrial IoT With Deep Reinforcement Learning.  ... 
doi:10.1109/tii.2020.2968165 fatcat:utk3ywxc6zgbdbfsys5f4otv7u

Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks

Gauri Kalnoor, Gowrishankar S
2021 Journal of Telecommunications and Information Technology  
In this paper, a new reinforcement learning intrusion detection system is developed for IoT networks incorporated with WSNs.  ...  Computational analysis is performed, and then the results are compared with the current methodologies, i.e. distributed denial of service (DDoS) attack.  ...  The continuity of state and action space due to the high dimensionality is considered by the author where deep reinforcement learning based dynamic resource management (DDRM) algorithm is proposed.  ... 
doi:10.26636/jtit.2021.151221 fatcat:p6gda3ztibchdlv4wwx7tiua74

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Leila Ismail, Rajkumar Buyya
2022 Sensors  
to satisfy a Service-Level-Agreement (SLA) for the end users.  ...  the network generation systems from 1G to AI-enabled 6G and its employed self-learning models.  ...  Deep Reinforcement Learning (DRL)-based data caching in the Internet of Things (IoT) environment. Figure 9 . 9 Figure 9.  ... 
doi:10.3390/s22155750 fatcat:kecdmc72b5cejkkkcm7fmtv54u

Hybrid Approaches to Address Various Challenges in Wireless Sensor Network for IoT Applications: Opportunities and Open Problems

Pallavi Joshi, Ajay Singh Raghuvanshi
2021 International Journal of Computer Networks And Applications  
To conquer the limitations of traditional WSN algorithms, machine learning has been introduced in wireless technology.  ...  It also elucidates some open issues for WSNs/IoT networks that can be solved using these approaches. SURVEY ARTICLE with IoT.  ...  minimal optimization (SMO) along with support vector machines (SVM) for binary classification and optimally-pruned extreme learning machine (OP-ELM) for multiclass classification of anomalies  ... 
doi:10.22247/ijcna/2021/209186 fatcat:isjbrui3wjb6bcj5ifyzt6jboi

Table of Contents

2022 IEEE Systems Journal  
Li Transfer Learning for Autonomous Cell Activation Based on Relational Reinforcement Learning With Adaptive Reward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Zhang Deep Reinforcement Learning-Based Mobility-Aware UAV Content Caching and Placement in Mobile Edge Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S.  ... 
doi:10.1109/jsyst.2022.3144812 fatcat:yabqtx64xfe4xkh6ptvnfqdjji

2020 Index IEEE Transactions on Cybernetics Vol. 50

2020 IEEE Transactions on Cybernetics  
., Reference Trajectory Reshaping Optimi-zation and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation; TCYB Aug. 2020 3740-3751 Wu, X., Jiang, B., Yu, K., Miao, c., and Chen, H  ...  ., +, TCYB June 2020 2872-2885 Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning.  ...  ., +, TCYB Sept. 2020 3950-3962 Cooling Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning.  ... 
doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a
« Previous Showing results 1 — 15 out of 139 results