Filters








84 Hits in 8.8 sec

On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Hang Qi, Hao Huang, Zhiqun Hu, Xiangming Wen, Zhaoming Lu
2020 Sensors  
In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel  ...  To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB.  ...  According to the above analyses, in this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) [14] to select channel bonding parameter for heterogeneous  ... 
doi:10.3390/s20102789 pmid:32422964 pmcid:PMC7284444 fatcat:ylo6ki4qmrfjhaieaouj3jul4m

Stateless Reinforcement Learning for Multi-Agent Systems: the Case of Spectrum Allocation in Dynamic Channel Bonding WLANs [article]

Sergio Barrachina-Muñoz, Alessandro Chiumento, Boris Bellalta
2021 arXiv   pre-print
Spectrum allocation in the form of primary channel and bandwidth selection is a key factor for dynamic channel bonding (DCB) wireless local area networks (WLANs).  ...  learning (RL) given its trial-and-error approach.  ...  Finally RL's combination with deep learning results in deep reinforcement learning (DRL), where the policy or algorithm uses a deep neural network rather than tables.  ... 
arXiv:2106.05553v1 fatcat:5ka6ucgkqrhhbdcgje2r6e64ky

Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs

Sergio Barrachina-Munoz, Alessandro Chiumento, Boris Bellalta
2021 IEEE Access  
While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary  ...  In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning  ...  MULTI-ARMED BANDITS Among model-free RL approaches, we highlight MABs, Temporal Difference (TD) methods like Q-learning, and deep reinforcement learning (DRL) methods like deep Q-learning (DQN). 4 Fig.  ... 
doi:10.1109/access.2021.3114430 fatcat:cjkmrvht6zh53pvoexzrsukylm

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs [article]

Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz, Emre Ozfatura, Kerem Ozfatura, Ozlem Yildiz, Deniz Gündüz, Hao Chen, Xiaoying Ye, Lizhao You, Yulin Shao, Paolo Dini (+1 others)
2022 arXiv   pre-print
In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs).  ...  More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance through federated learning (FL) models.  ...  For those reasons, in this paper, we focus on the suitability of supervised learning methods, mostly based on deep learning (DL), for the SR problem in WLANs.  ... 
arXiv:2203.10472v2 fatcat:lcbcnyaqufeyzaqgcs4y23uiyu

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.  ...  Then, considering the goal of minimizing latency, a on-demand channel bonding (DCB) algorithm that uses DRL, along with a multi-agent deep deterministic policy gradient (MADDPG) for training, to find suitable  ... 
arXiv:2109.04786v3 fatcat:ny55qfhsnfduzcxyve5mylpr2m

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

Szymon Szott, Katarzyna Kosek-Szott, Piotr Gawlowicz, Jorge Torres Gomez, Boris Bellalta, Anatolij Zubow, Falko Dressler
2022 IEEE Communications Surveys and Tutorials  
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.  ...  Then, considering the goal of minimizing latency, a on-demand channel bonding (DCB) algorithm that uses DRL, along with a multi-agent deep deterministic policy gradient (MADDPG) for training, to find suitable  ... 
doi:10.1109/comst.2022.3179242 fatcat:sqmcwxuawrchjkaprnak4kawym

Table of Contents

2020 IEEE Transactions on Vehicular Technology  
Wireless Networks Learning to Bond in Dense WLANs With Random Traffic Demands. . . . . . . . . . . . . . . . . . . . . . . . . . .Y. Luo and K.-W.  ...  So 11584 UAV-Enabled Secure Communications by Multi-Agent Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2020.3027076 fatcat:xns72myvtfhgpjy622lpgmii3i

2020 Index IEEE Transactions on Mobile Computing Vol. 19

2021 IEEE Transactions on Mobile Computing  
., +, TMC Sept. 2020 2076-2087 Dynamic Channel Bonding in Spatially Distributed High-Density WLANs.  ...  ., +, TMC June 2020 1274-1285 DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networks.  ... 
doi:10.1109/tmc.2020.3036773 fatcat:6puiux5lp5bfvjo47ey7ycwyfu

2021 Index IEEE Transactions on Wireless Communications Vol. 20

2021 IEEE Transactions on Wireless Communications  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  ., +, TWC Feb. 2021 1363-1378 Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks.  ... 
doi:10.1109/twc.2021.3135649 fatcat:bgd3vzb7pbee7jp75dnbucihmq

Next generation IEEE 802.11 Wireless Local Area Networks: Current status, future directions and open challenges

Boris Bellalta, Luciano Bononi, Raffaele Bruno, Andreas Kassler
2016 Computer Communications  
, dynamic channel bonding, spectrum databases and channel sensing, enhanced power saving mechanisms and efficient small data transmissions.  ...  In contrast to other IEEE 802.11 surveys, this is a use case oriented study. Specifically, we first describe the three key scenarios in which next-generation WLANs will have to operate.  ...  A special thanks also to Toke Høiland-Jørgensen and Ognjen Dobrijević for the time they spent reviewing the manuscript.  ... 
doi:10.1016/j.comcom.2015.10.007 fatcat:fqyw73iqbbfmvcpyawnkfkakt4

2020 Index IEEE Transactions on Vehicular Technology Vol. 69

2020 IEEE Transactions on Vehicular Technology  
+ Check author entry for coauthors ami-mFading Channels With Integer and Non-Integerm; TVT March 2020 2785-2801 Hoang, T.M., Tran, X.N., Nguyen, B.C., and Dung, L.T., On the Performance of MIMO Full-Duplex  ...  VANETs; TVT Dec. 2020 15933-15946 Hoseini, S.A., Ding, M., Hassan, M., and Chen, Y., Analyzing the Impact of Molecular Re-Radiation on the MIMO Capacity in High-Frequency Bands; TVT Dec. 2020 15458-15471  ...  ., +, TVT Dec. 2020 15019-15034 Learning to Bond in Dense WLANs With Random Traffic Demands.  ... 
doi:10.1109/tvt.2021.3055470 fatcat:536l4pgnufhixneoa3a3dibdma

Reinforcement learning on graphs: A survey [article]

Nie Mingshuo, Chen Dongming, Wang Dongqi
2022 arXiv   pre-print
As far as we know, this is the latest work on a comprehensive survey of GRL, this work provides a global view and a learning resource for scholars.  ...  In this survey, we provide a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation.  ...  [102] propose a policybased agent to extend its reasoning paths sequentially through a RL approach on the multi-hop reasoning task.  ... 
arXiv:2204.06127v2 fatcat:7wf6qxnxzza7xbiwjgjmrsrdjq

2019 Index IEEE Transactions on Communications Vol. 67

2019 IEEE Transactions on Communications  
Ketseoglou, T., +, TCOMM Oct. 2019 6816-6828 Deep learning Sensing OFDM Signal: A Deep Learning Approach.  ...  ., +, TCOMM Nov. 2019 7950-7965 Sensing OFDM Signal: A Deep Learning Approach.  ...  S Satellite antennas On the Performance of LMS  ... 
doi:10.1109/tcomm.2019.2963622 fatcat:qd6so3reavde3eiukfknmpbfsu

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  ...  Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution.  ...  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

2020 Index IEEE Transactions on Industrial Informatics Vol. 16

2020 IEEE Transactions on Industrial Informatics  
TII Nov. 2020 7056-7066 Jiang, L., see Cai, H., TII Jan. 2020 587-594 Jiang, L., see Xia, Z., TII Jan. 2020 629-638 Jiang, Q., Yan, S., Yan, X., Yi, H., and Gao, F., Data-Driven Two-Dimensional Deep  ...  Correlated Representation Learning for Nonlinear Batch Process Monitoring; TII April 2020 2839-2848 Jiang, S., see Li, Y., 1076-1085 Jiang, X., see Gong, K., 1625-1634 Jiang, X., see Xiao, J., TII April  ...  ., +, TII Feb. 2020 1343-1351 DeepWelding: A Deep Learning Enhanced Approach to GTAW Using Multi- source Sensing Images.  ... 
doi:10.1109/tii.2021.3053362 fatcat:blfvdtsc3fdstnk6qoaazskd3i
« Previous Showing results 1 — 15 out of 84 results