Machine Learning in NextG Networks via Generative Adversarial Networks

#Machine #Learning #(ML) #algorithms #Generative #Adversarial #Networks #(GANs)address #competitive #resource #allocation #problems #next-generation #(NextG) #communications #cognitive #networks #to #address #i) #spectrum #sharing #ii) #detecting #anomalies #and #iii) #mitigating #security #attacks
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Generative Adversarial Networks (GANs) implement Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this talk, we discuss their use in next-generation (NextG) communications within the context of cognitive networks to address i) spectrum sharing, ii) detecting anomalies, and iii) mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and nonrepeatable. Second, they enable pre-training classifiers by using semi supervised data. Third, they facilitate increased resolution. Fourth, they enable recovering corrupted bits in the spectrum. The talk will provide basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for i)–iii) above, some simulation results, and future research directions. In the spectrum sharing problem, connections to cognitive wireless networks are established. Simulation results show that a particular GAN implementation is better than a convolutional autoencoder for an outlier detection problem in spectrum sensing.


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  • Date: 21 May 2025
  • Time: 11:00 PM UTC to 01:00 AM UTC
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  • Starts 04 March 2025 05:00 AM UTC
  • Ends 21 May 2025 04:00 AM UTC
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Machine Learning in NextG Networks via Generative Adversarial Networks

Biography:

Ender Ayanoglu received the Ph.D. degree from Stanford University, Stanford, CA in 1986, in electrical engineering. He was with the Communications Systems Research Laboratory, part of AT&T Bell Laboratories, Holmdel, NJ until 1996, and Bell Labs, Lucent Technologies until 1999. From 1999 until 2002, he was a Systems Architect at Cisco Systems, Inc., San Jose, CA. Since 2002, he has been a Professor in the Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, where he served as the Director of the Center for Pervasive Communications and Computing and held the Conexant-Broadcom Endowed Chair during 2002-2010. From 1993 until 2014, Dr. Ayanoglu was an Editor of the IEEE Transactions on Communications. He served as the Editor-in-Chief of the IEEE Transactions on Communications from 2004 to 2008. From January 2015 until December 2016, he served as the Editor-in-Chief of the IEEE Journal on Selected Areas in Communications - Series on Green Communications and Networking. He served as the Founding Editor-in-Chief of the IEEE Transactions on Green Communications and Networking from August 2016 to August 2020. From 1990 to 2002, he served on the Executive Committee of the IEEE Communications Society Communication Theory Committee, and from 1999 to 2002, was its Chair. Dr. Ayanoglu is the recipient of the IEEE Communications Society Stephen O. Rice Prize Paper Award in 1995, the IEEE Communications Society Best Tutorial Paper Award in 1997, the IEEE Communications Society Communication Theory Technical Committee Outstanding Service Award in 2014, and the IEEE Communications Society Joseph LoCicero Award in 2023. He has been an IEEE Fellow since 1998. He has served as an IEEE Communications Society Distinguished Lecturer in 2022-2023 and in 2024-2025.

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