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, the speaker will 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 semisupervised 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. Co-sponsored by: Vishnu S. Pendyala, SJSU Speaker(s): Dr. Vishnu S. Pendyala, Prof. Ender Ayanoglu Virtual: https://events.vtools.ieee.org/m/493301

August ExCom 733 Bishop Street, Suite 2000, Honolulu, Hawaii, United States, Virtual: https://events.vtools.ieee.org/m/485243

Professor Azita Emami from The California Institute of Technology will give a seminar talk on "Brain-Computer Interfaces: A Software-Hardware Co-Design Approach" on Wednesday August 27th at 6:30PM. RSVP one week in advance for food orders, provided for $5 cash per person. Students do not need to bring payment. Indicate food preference with event registration. Room: 244, Bldg: Holmes Hall, 2540 Dole St, Honolulu, Hawaii, United States, 96822