Title:
AI-Based Physical Layer Security Processing for 6G Privacy

dc.contributor.author Kelley, Brian
dc.contributor.corporatename Georgia Institute of Technology. Institute for Information Security & Privacy en_US
dc.contributor.corporatename University of Texas at San Antonio. Dept. of Electrical and Computer Engineering en_US
dc.date.accessioned 2022-01-27T21:58:45Z
dc.date.available 2022-01-27T21:58:45Z
dc.date.issued 2022-01-21
dc.description Presented online via Bluejeans Events on January 21, 2022 at 12:30 p.m. en_US
dc.description Brian Kelley is an Associate Professor in the Department of Electrical and Computer Engineering at University of Texas at San Antonio. His research interests are 4G Cellular Communications, signal processing algorithms for wireless communications, 3G cellular, OFDMA, MIMO, ultra wideband communications, systems on a integrated circuit chip (SoC), digital radios, software definable radio (SDR), object oriented modeling of large scale communication systems, application specific DSP processors, system level design, and high speed computer arithmetic. en_US
dc.description Runtime: 58:36 minutes en_US
dc.description.abstract This research addresses the challenge of privacy in 6G wireless networks using Artificial Intelligence. Privacy and prevention of eavesdropping are significant concerns in 6G networks, warranting motivation for new approaches, such as Physical Layer Security. Physical Layer Security, applied at Layer 1 of the OSI stack, provides low-latency privacy for over-the-air waveforms at the transmit antenna. This research describes a new application of Key Based Physical Layer Security (KB-PLS), capable of mapping secret information bits onto MIMO precoders derived from codebooks. This work defines codebook precoder classification as a problem that can be solved using neural networks. We describe a communication protocol in which Singular Value Decomposition (SVD) recovers precoder values. The receiver applies machine learning to recover bits by matching noisy precoders to specific codebook entries. Aided by accelerating technologies such as NVIDIA GPUs and cuDNN libraries, machine learning methods are discussed that replace and improve upon existing mathematical derivations of the 6G Privacy protocol. en_US
dc.format.extent 58:36 minutes
dc.identifier.uri http://hdl.handle.net/1853/66206
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.relation.ispartofseries Cybersecurity Lecture Series
dc.subject 5G en_US
dc.subject 6G en_US
dc.subject Artificial intelligence (AI) en_US
dc.subject Physical layer security en_US
dc.title AI-Based Physical Layer Security Processing for 6G Privacy en_US
dc.type Moving Image
dc.type.genre Lecture
dspace.entity.type Publication
local.contributor.corporatename School of Cybersecurity and Privacy
local.contributor.corporatename College of Computing
local.relation.ispartofseries Institute for Information Security & Privacy Cybersecurity Lecture Series
relation.isOrgUnitOfPublication f6d1765b-8d68-42f4-97a7-fe5e2e2aefdf
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isSeriesOfPublication 2b4a3c7a-f972-4a82-aeaa-818747ae18a7
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