Reminder: Machine Learning in NextG Networks via Generative Adversarial Networks : vTools eNotice

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Reminder: Machine Learning in NextG Networks via Generative Adversarial Networks

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Machine Learning in NextG Networks via Generative Adversarial Networks

Generative Adversarial Networks (GANs) implement Machine Learning (ML) algorithms that can 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 non-repeatable. 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, several 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 auto encoder for an outlier detection problem in spectrum sensing.

Date and Time

Date: 03 Jun 2025
Time: 07:00 PM to 08:00 PM
All times are (UTC-07:00) Pacific Time (US & Canada)
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This event has virtual attendance info. Please visit the event page to attend virtually.

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Registration

Starts 03 February 2025 12:00 AM
Ends 03 June 2025 12:00 AM
All times are (UTC-07:00) Pacific Time (US & Canada)
No Admission Charge

Speakers

Ender Ayanoglu

Attachments:

Utah Section Jt. Chapter, SP01/COM19 : http://ewh.ieee.org/mu/utah/societies-and-chapters/

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