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Bandit Sampling for Multiplex Networks [article]

Cenk Baykal, Vamsi K. Potluru, Sameena Shah, Manuela M. Veloso
2022 arXiv   pre-print
We propose an algorithm for scalable learning on multiplex networks with a large number of layers.  ...  In particular, they are used for node classification and link prediction which have a wide range of applications in social networks, biomedical data sets, and financial transaction graphs.  ...  To put it all together in the case of multiplex layer sampling, the idea is to have a separate instance of Alg. 1 for each layer of the network and update the sampling distribution accordingly as shown  ... 
arXiv:2202.03621v1 fatcat:u5onec3xkrdrne4woilw6hvcri

Crawling the Community Structure of Multiplex Networks

Ricky Laishram, Jeremy D. Wendt, Sucheta Soundarajan
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We propose MultiComSample (MCS), a novel algorithm for crawling a multiplex network.  ...  MCS uses multiple levels of multi-armed bandits to determine the best layers, communities and node roles for selecting nodes to query.  ...  ., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.  ... 
doi:10.1609/aaai.v33i01.3301168 fatcat:tty2wsjpx5eybizcarx3pn7tea

ONETS: Online Network Slice Broker From Theory to Practice [article]

Vincenzo Sciancalepore, Lanfranco Zanzi, Xavier Costa-Perez, Antonio Capone
2020 arXiv   pre-print
considering three network slices and (v) allows for the design of a low-complexity online network slice brokering solution that maximizes multiplexing gains.  ...  In this paper we propose and analyze ONETS: an Online NETwork Slicing solution that (i) builds on the budgeted lock-up multi-armed bandit mathematical model and properties, (ii) derives its analytical  ...  fashion, pursuing the objective of network slicing multiplexing maximization while still honouring the agreed guarantees (SLAs) for previously granted network slice requests.  ... 
arXiv:1801.03484v2 fatcat:fzjevogn6bfclae7ez2drpa3jq

ONETS: Online Network Slice Broker From Theory to Practice

Vincenzo Sciancalepore, Lanfranco Zanzi, Xavier Costa-Perez, Antonio Capone
2021 IEEE Transactions on Wireless Communications  
three network slices and v) allows for the design of a low-complexity online network slice brokering solution that maximizes multiplexing gains.  ...  In this paper, we propose and analyze ONETS, an Online NETwork Slicing solution that i) builds on the budgeted lock-up multiarmed bandit mathematical model and properties, ii) derives its analytical bounds  ...  Fig. 9 . 9 Sample of video surveillance face detection: successful (upper side) and failed (lower side).  ... 
doi:10.1109/twc.2021.3094116 fatcat:kb63dmmjbvblvivhrpho5e3dka

Learning Frameworks for Dynamic Joint RF Energy Harvesting and Channel Access

Fahira Sangare, Duy H. N. Nguyen, Zhu Han
2019 IEEE Access  
The fifth generation mobile networks (5G) envision to interconnect the massive number of devices with a wide range of characteristics and demands for 2020 and beyond.  ...  The system's objective is to use the harvested energy for concurrent data transmission. We model the selection of channels to maximize this objective as a multi-armed bandit (MAB) problem.  ...  The sample means for the rewards r n (t) and r m (t) are also updated accordingly.  ... 
doi:10.1109/access.2019.2925281 fatcat:lqewnlhd45dcblmmjgm6arzsqu

Table of Contents

2020 IEEE Transactions on Network Science and Engineering  
Feng 1788 Combinatorial Sleeping Bandits With Fairness Constraints . and J.  ...  Raz 1687 Community Detection and Improved Detectability in Multiplex Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tnse.2020.3003537 fatcat:mujbc3zvzrcsxivq4pqb4v5bd4

User pairing using laser chaos decision maker for NOMA systems

Zengchao Duan, Aohan Li, Norihiro Okada, Yusuke Ito, Nicolas Chauvet, Makoto Naruse, Mikio Hasegawa
2022 Nonlinear Theory and Its Applications IEICE  
Non-Orthogonal Multiple Access is one of the most important technologies in 5G and Beyond 5G wireless communications, which improve system performance by power domain multiplexing.  ...  In the meantime, ultrafast methods of solving multi-armed bandit problems have been developed using chaotic laser time series.  ...  In [1] , the laser chaos time series is utilized in solving two-armed bandit problems at ultrahigh-speed, of which scalability is later accomplished by time-domain multiplexing [2] .  ... 
doi:10.1587/nolta.13.72 fatcat:maqdxrr2drczjk4iymfhuoliqy

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos [article]

Makoto Naruse, Takatomo Mihana, Hirokazu Hori, Hayato Saigo, Kazuya Okamura, Mikio Hasegawa, Atsushi Uchida
2018 arXiv   pre-print
In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time-series.  ...  This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.  ...  Advanced Research Networks and the Grants-in-  ... 
arXiv:1803.09425v1 fatcat:emsfpcocovg6folfzbw5biwcam

Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series [article]

Shungo Takeuchi, Mikio Hasegawa, Kazutaka Kanno, Atsushi Uchida, Nicolas Chauvet, Makoto Naruse
2019 arXiv   pre-print
This study provides a first step toward the application of ultrafast chaotic lasers for future high-performance wireless communication networks.  ...  Multi-armed bandit (MAB) algorithms are a promising approach by which the difficult tradeoff between exploration to search for better a channel and exploitation to experience enhanced communication quality  ...  Advanced Research Networks and Grants-in-Aid for Scientific Research (JP17H01277 and JP19H00868) funded by the Japan Society for the Promotion of Science. Author Contributions  ... 
arXiv:1909.03629v1 fatcat:bmxtukdkrbftfl3qbt2axslj5y

Learning State Selection for Reconfigurable Antennas: A Multi-Armed Bandit Approach

Nikhil Gulati, Kapil R. Dandekar
2014 IEEE Transactions on Antennas and Propagation  
In order to utilize the benefits of reconfigurable antennas, selecting an optimal antenna state for communication is essential and depends on the availability of full channel state information for all  ...  Reconfigurable antennas are capable of dynamically re-shaping their radiation patterns in response to the needs of a wireless link or a network.  ...  ACKNOWLEDGMENT The authors wish to acknowledge Kevin Wanuga, David Gonzalez, and Rohit Bahl for their valuable suggestions and feedback.  ... 
doi:10.1109/tap.2013.2276414 fatcat:l6t3jilybnbn3f2x52mrtpqf5a

Arm order recognition in multi-armed bandit problem with laser chaos time series

Naoki Narisawa, Nicolas Chauvet, Mikio Hasegawa, Makoto Naruse
2021 Scientific Reports  
the time-division multiplexing of laser chaos time series.  ...  AbstractBy exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing  ...  the Japan Society for the Promotion of Science.  ... 
doi:10.1038/s41598-021-83726-8 pmid:33627692 pmcid:PMC7904956 fatcat:w5ke7f4xhbeflj5mmhaouhdzeq

Scalable photonic reinforcement learning by time-division multiplexing of laser chaos

Makoto Naruse, Takatomo Mihana, Hirokazu Hori, Hayato Saigo, Kazuya Okamura, Mikio Hasegawa, Atsushi Uchida
2018 Scientific Reports  
In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time series.  ...  This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.  ...  Advanced Research Networks and the Grants-in-Aid for Scientific Additional Information  ... 
doi:10.1038/s41598-018-29117-y pmid:30022085 pmcid:PMC6052166 fatcat:vzl2pnfnl5dfxlg4styn7sqj5e

Table of contents

2021 IEEE Transactions on Circuits and Systems - II - Express Briefs  
Pilipko 883 A TD-ADC for IR-UWB Radars With Equivalent Sampling Technology and 8-GS/s Effective Sampling Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Paz 1003 Intelligent and Reconfigurable Architecture for KL Divergence-Based Multi-Armed Bandit Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tcsii.2021.3055317 fatcat:sqqpvgoqbzgpfcwqii3yjkdfiq

Cognitive Radio and Dynamic Spectrum Sharing Systems

Ivan Cosovic, Friedrich K. Jondral, Milind M. Buddhikot, Ryuji Kohno
2008 EURASIP Journal on Wireless Communications and Networking  
It is shown that in terms of spectral efficiency per Watt, sequential transmission is always preferable to simultaneous transmission for powerconstrained wireless networks.  ...  By extending cognitive radio principles to a network layer, a concept of cognitive networks arises.  ...  Bahai investigate the problem of optimal channel selection for spectrum-agile low-powered wireless networks in unlicensed bands in the tenth paper "Optimal channel selection for spectrum-agile low-power  ... 
doi:10.1155/2008/278016 fatcat:vprbcaqitjanfc37h4c2sfbsf4

Distributed Online Learning for Coexistence in Cognitive Radar Networks [article]

William Howard, Anthony Martone, R. Michael Buehrer
2022 arXiv   pre-print
For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center  ...  This work addresses the coexistence problem for radar networks.  ...  Specifically we will develop the use of a multi-player bandit algorithm for center frequency selection, then delegate the choice of a waveform to a single player bandit, with independent instances for  ... 
arXiv:2203.02327v2 fatcat:lfiz4asobnaklakfc3mj2m6spi
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