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SUMMARY:Integrated Sensing and Communication Systems for 6G: Topics on Wav
 eform Design\, Reinforcement Learning and Signal Processing
DTSTART:20221019T161500
DTEND:20221019T171500
DTSTAMP:20260407T003757Z
UID:dbad7fce176e58b8bd0410a7004c90195792855e1e0c6832cf44dc07
CATEGORIES:Conferences - Seminars
DESCRIPTION:Visa Koivunen (IEEE Fellow\, EURASIP Fellow) received his D.Sc
 . (EE) degree with honors from the Univ of Oulu\, Dept. of Electrical Engi
 neering. He was a visiting researcher at the Univ of Pennsylvania\, Philad
 elphia\, USA\, 1991-1995 and adjunct full professor in 2003-2006. Since 19
 99 he has been a full Professor of Signal Processing at Aalto University (
 formerly HUT)\, Finland. He received the Academy professor position in 201
 0 and Aalto Distinguished professor in 2020. During his sabbatical terms i
 n 2006-2007 and 2013-2014 he was a visiting faculty at Princeton Universit
 y and has had many mini-sabbaticals there over the years. He has also been
  a Visiting Fellow at Nokia Research (2006-2012). On his sabbatical term i
 n 2022-23\, he is visiting professor at EPFL\, Lausanne\, Switzerland.\n\n
  \nIntegrated Sensing and Communications Systems (ISAC) perform radio fre
 quency sensing and transfer wireless data jointly. They operate in a share
 d and congested spectrum. We are considering ISAC systems that cooperate o
 r are co-designed for mutual benefits. Co-designed systems may share HW an
 d antenna resources as well as awareness about the state of the radio spec
 trum. The ISAC systems have a number of operational parameters that can be
  adjusted either by using structured optimization or machine learning. We 
 focus on multicarrier waveforms used by most current and emerging wireless
  communication systems. Similarly\, multicarrier waveforms have been emplo
 yed for radar purposes. We will present waveform optimization\, machine le
 arning\, interference management and signal processing methods for co-desi
 gned ISAC systems that share spectrum awareness. Model-based reinforcement
  learning approach is taken to exploit the rich structural knowledge of ma
 n-made communication and sensing systems and propagation effects to choose
  optimal actions. Optimization methods impose constraints that ensure mini
 mum desired performance levels for other sub-systems. The developed OFDM r
 adar signal processing algorithms in ISAC can take advantage of nonidealit
 ies such as carrier offsets that are commonly considered an impairment in 
 wireless communications. We demonstrate the achieved performance gains in 
 different sensing and communication tasks and interference management thro
 ugh extensive simulation and analytical results.\n 
LOCATION:BC 129 https://plan.epfl.ch/?room==BC%20129
STATUS:CONFIRMED
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