BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Africa/Johannesburg
BEGIN:STANDARD
DTSTART:19440319T010000
TZOFFSETFROM:+0300
TZOFFSETTO:+0200
TZNAME:SAST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20251124T144449Z
UID:E3A4A734-E57A-426E-8E93-DBE9F64CED4F
DTSTART;TZID=Africa/Johannesburg:20251124T150000
DTEND;TZID=Africa/Johannesburg:20251124T160000
DESCRIPTION:In our increasingly connected cities\, vehicular communication 
 is key for safety and efficiency. However\, in multicell Visible Light Com
 munication (VLC) systems\, a critical fairness issue arises: vehicles at t
 he edge of a communication cell experience drastically slower data rates d
 ue to weak signals and interference from adjacent cells. This digital "slo
 w lane" compromises the reliability of the entire network.\n\nThis present
 ation introduces a novel cooperative transmission framework that addresses
  this challenge head-on. By integrating Optical Intelligent Reflecting Sur
 faces (OIRS) - essentially smart mirrors for light - we can create new\, r
 obust communication pathways and intelligently mitigate inter-cell interfe
 rence. We will explore how this architecture transforms the network from a
  collection of isolated cells into a collaborative system. The core of thi
 s work is a resource allocation problem formulated to maximize the minimum
  data rate for any vehicle in the system\, a concept known as max-min fair
 ness. I will walk through the proposed algorithm\, which efficiently solve
 s this complex problem by decomposing it into three manageable subproblems
 : OIRS assignment\, subchannel allocation\, and power adjustment. These ar
 e solved iteratively using a block coordinate descent method.\n\nThrough s
 imulation results\, we will see that this cooperative OIRS-assisted approa
 ch significantly outperforms existing baseline methods. The findings confi
 rm a substantial improvement in max-min fairness\, ensuring more consisten
 t and reliable connectivity for all vehicles\, regardless of their positio
 n. This work is a crucial step toward deploying fair and efficient vehicul
 ar VLC systems\, paving the way for the next generation of intelligent tra
 nsportation networks.\n\nThis presentation is based on the paper:\n"Resour
 ce Allocation for Fairness Enhancement in Multicell Vehicular VLC System W
 ith Optical IRS: A Cooperative Transmission Approach\," by N. An\, F. Yang
 \, C. Liu\, L. Cheng\, J. Song\, and Z. Han\, published in the IEEE Intern
 et of Things Journal\, vol. 12\, no. 11\, pp. 16931-16946\, June 2025.\n\n
 Speaker(s): \, Ling Cheng\n\nVirtual: https://events.vtools.ieee.org/m/512
 581
LOCATION:Virtual: https://events.vtools.ieee.org/m/512581
ORGANIZER:filip.paluncic@up.ac.za
SEQUENCE:15
SUMMARY:Smart Mirrors on the Road: Eliminating Dead Zones in Vehicular Ligh
 t-Based Networks
URL;VALUE=URI:https://events.vtools.ieee.org/m/512581
X-ALT-DESC:Description: <br /><div>In our increasingly connected cities\, v
 ehicular communication is key for safety and efficiency. However\, in mult
 icell Visible Light Communication (VLC) systems\, a critical fairness issu
 e arises: vehicles at the edge of a communication cell experience drastica
 lly slower data rates due to weak signals and interference from adjacent c
 ells. This digital "slow lane" compromises the reliability of the entire n
 etwork.</div>\n<div>&nbsp\;</div>\n<div>This presentation introduces a nov
 el cooperative transmission framework that addresses this challenge head-o
 n. By integrating Optical Intelligent Reflecting Surfaces (OIRS) - essenti
 ally smart mirrors for light - we can create new\, robust communication pa
 thways and intelligently mitigate inter-cell interference. We will explore
  how this architecture transforms the network from a collection of isolate
 d cells into a collaborative system.&nbsp\;The core of this work is a reso
 urce allocation problem formulated to maximize the minimum data rate for a
 ny vehicle in the system\, a concept known as max-min fairness. I will wal
 k through the proposed algorithm\, which efficiently solves this complex p
 roblem by decomposing it into three manageable subproblems: OIRS assignmen
 t\, subchannel allocation\, and power adjustment. These are solved iterati
 vely using a block coordinate descent method.</div>\n<div>&nbsp\;</div>\n<
 div>Through simulation results\, we will see that this cooperative OIRS-as
 sisted approach significantly outperforms existing baseline methods. The f
 indings confirm a substantial improvement in max-min fairness\, ensuring m
 ore consistent and reliable connectivity for all vehicles\, regardless of 
 their position. This work is a crucial step toward deploying fair and effi
 cient vehicular VLC systems\, paving the way for the next generation of in
 telligent transportation networks.</div>\n<div>&nbsp\;</div>\n<div>This pr
 esentation is based on the paper:</div>\n<div>"Resource Allocation for Fai
 rness Enhancement in Multicell Vehicular VLC System With Optical IRS: A Co
 operative Transmission Approach\," by N. An\, F. Yang\, C. Liu\, L. Cheng\
 , J. Song\, and Z. Han\, published in the&nbsp\;<em>IEEE Internet of Thing
 s Journal</em>\, vol. 12\, no. 11\, pp. 16931-16946\, June 2025.</div>
END:VEVENT
END:VCALENDAR