BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:PhD Final Exam – Abdelkader Aljerme
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20261007T212416Z
UID:tag:localist.com\,2008:EventInstance_32945282766281
DTSTART:20200319T190000Z
DTEND:20200319T210000Z
DESCRIPTION:Advanced Time-varying approaches for Modeling the Multipath Cha
 nnel in Wireless Network\n\nThis dissertation proposes the use of advanced
  time-varying approaches for modeling the dynamics of the multipath channe
 l in wireless communication networks. These advanced time-varying approach
 es include linear Kalman innovation models in observable block companion f
 orm\, and neural network-based models. The effectiveness of these type of 
 models is evaluated through three case studies. The first case study invol
 ves the identification of a linear time-varying Kalman innovation model\, 
 for describing measured received signal strength (RSSI) as a function of t
 he speed of the link in an indoor multipath wireless channel. Results for 
 this first case study show that the model exhibits both accuracy and robus
 tness. The second case study evaluates the suitability of using a linear t
 ime-varying Kalman innovation model of the RSSI\, for secret key generatio
 n in the physical layer of multipath wireless channels. It was found that 
 the residuals of the Kalman model\, due to! their significant randomness\,
  exhibit a notable potential for secret key generation\; indeed\, improved
  values of maximum channel capacity for secret key generation were achieve
 d. At last\, the third case study includes the identification of a neural 
 network-based autoregressive moving average with exogenous inputs (NN-ARMA
 X) model and of a neural network-based autoregressive with exogenous input
 s (NN-ARX) model\, for describing traffic in a 4G-LTE network. Both models
  showed similar performance\, but the NN-ARMAX has the advantage that it c
 an be converted to a linear time-varying Kalman innovation model\, and thu
 s can be used for the implementation of advanced strategies for controllin
 g the operation of the network.\n\nMajor Advisor: Huaping Liu\nCommittee: 
 Bella Bose\nCommittee: Eduardo Cottilla-Sanchez\nCommittee: Tejasvi Anand\
 nGCR: David Porter
GEO:44.567164;-123.278692
LOCATION:Kelley Engineering Center\, 1007
SUMMARY:PhD Final Exam – Abdelkader Aljerme
URL;VALUE=URI:https://events.oregonstate.edu/event/phd_final_exam_abdelkade
 r_aljerme
CATEGORIES:Lecture or Presentation
END:VEVENT
END:VCALENDAR