BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Israel
BEGIN:DAYLIGHT
DTSTART:20220325T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0300
RRULE:FREQ=YEARLY;BYDAY=-1FR;BYMONTH=3
TZNAME:IDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20211031T010000
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:IST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20221230T130737Z
UID:4217730F-8F78-454E-B379-1BE0498821A0
DTSTART;TZID=Israel:20211215T100000
DTEND;TZID=Israel:20211215T120000
DESCRIPTION:Digital predistortion (DPD) has been widely adopted to keep RF 
 power amplifier operating with high efficiency without losing linearity in
  the exiting 4G systems. It is expected that DPD will continue to be deplo
 yed in 5G systems. However\, due to shifting from the single antenna to th
 e multiple-input multiple-output (MIMO) phased array and continuously incr
 eased signal bandwidth\, system designers face significant challenges in m
 anaging power consumption and meeting linearity requirement of wireless tr
 ansmitters. In this talk\, we will discuss how the recently developed mach
 ine learning techniques can be utilised to resolve some of the issues in l
 inearizing 5G MIMO systems\, including data-clustering assisted DPD for mu
 ltiple dynamic configurations\, model tree-based behavioural model constru
 ction and simplified model extraction techniques.\n\nSpeaker(s): Prof Andi
 ng  Zhu\, \n\nVirtual: https://events.vtools.ieee.org/m/297808
LOCATION:Virtual: https://events.vtools.ieee.org/m/297808
ORGANIZER:marias@technion.ac.il
SEQUENCE:1
SUMMARY:Digital Predistortion for 5G MIMO Transmitters Using Machine Learni
 ng
URL;VALUE=URI:https://events.vtools.ieee.org/m/297808
X-ALT-DESC:Description: <br /><p>Digital predistortion (DPD) has been widel
 y adopted to keep RF power amplifier operating with high efficiency withou
 t losing linearity in the exiting 4G systems. It is expected that DPD will
  continue to be deployed in 5G systems.&nbsp\; However\, due to shifting f
 rom the single antenna to the multiple-input multiple-output (MIMO) phased
  array and continuously increased signal bandwidth\, system designers face
  significant challenges in managing power consumption and meeting linearit
 y requirement of&nbsp\; wireless&nbsp\; transmitters.&nbsp\; In this talk\
 , we will discuss how the recently developed machine learning techniques c
 an be utilised to resolve some of the issues in linearizing 5G MIMO system
 s\, including data-clustering assisted DPD for multiple dynamic configurat
 ions\, model tree-based behavioural model construction and simplified mode
 l extraction techniques.</p>
END:VEVENT
END:VCALENDAR