BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20260728T154735EDT-4205JAuZL4@132.216.98.100
DTSTAMP:20260728T194735Z
DESCRIPTION:Abstract\n\n'The reconfigurable intelligent surface (RIS) is co
 mposed of passive reflective elements designed to reconfigure the wireless
  propagation environment for the next generation of wireless communication
  systems. In RIS-assisted wireless communications\, the main goals include
  channel estimation to acquire the channel state information (CSI)\, and j
 oint beamforming and phase shift reconfiguration to improve the achievable
  data rates. Due to the design challenges of making passive reflective ele
 ments\, one challenge is to deal with hardware impairments (HIs) on the de
 sired phase shifts during signal propagation. Another challenge is that th
 e number of parameters to estimate increases with the number of passive el
 ements\, thereby introducing a larger channel estimation overhead trade-of
 f between pilot training duration and channel estimation accuracy. After c
 hannel estimation\, knowledge of the CSI will be acquired at the access po
 int (AP)\, this poses another challenge as the RIS controller that updates
  the phase shifts relies on information transfer from the AP to the RIS\, 
 introducing a communication overhead scaling linearly with number of eleme
 nts. The objective of this dissertation is to address these challenges in 
 RIS-assisted wireless communications.\n\nThe first study is on channel est
 imation in a full-duplex (FD) wireless communication system assisted by a 
 RIS with HI occurring at the transceivers and RIS elements. We propose an 
 unbiased channel estimator that requires knowledge of only the first and s
 econd order statistics of the HI\, for which we derive closed form express
 ions. The proposed estimator reduces to the maximum likelihood estimator i
 n the case of ideal hardware. We also describe simultaneous and non-simult
 aneous orthogonal pilot schemes that minimize the mean square error of the
  maximum likelihood estimator in the case of ideal hardware.\n\nThe next s
 tudies introduce a novel tensor signal model for channel estimation of a R
 IS-assisted communication model for half-duplex (HD)\, which is then exten
 ded to FD. For the HD and FD models\, we use tensor signal modelling techn
 iques to estimate all CSI involving the self-interference\, direct-path\, 
 and the RIS assisted channel links. We model the received signal as a tens
 or composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-
 RIS and the RIS assisted links\, extend the alternating least squares algo
 rithm to jointly estimate all channels\, then derive the corresponding Cra
 mér-Rao Bound (CRB). The proposed method provides a more accurate estimate
  by efficiently using all pilots transmitted throughout the full training 
 duration without turning the RIS “OFF” when comparing the same number of t
 otal pilots transmitted. For a sufficient number of transmitted pilots\, t
 he proposed method’s accuracy comes close to the CRB for the RIS channels 
 and attains the CRB for the direct-path and self-interference channels.\n
 \nThe final study is on the design of a deep learning (DL) architecture to
  implement joint phase shift compression and beamforming using knowledge o
 f the acquired CSI. We propose a model-based DL architecture to reduce the
  number of bits required for transmitting phase shift information from the
  AP to the RIS controller. The AP computes the phase shifts and compresses
  them into a binary control message that is sent to the RIS controller for
  element configuration. To help reduce beamformer mismatches caused by pha
 se shift compression errors\, the beamformer is updated with the actual (d
 ecompressed) RIS phase shifts. By unrolling the iterative weighted minimum
  mean square error (WMMSE) algorithm within the wireless communication-inf
 ormed DL architecture\, joint phase shift compression and beamforming can 
 be trained end-to-end. The proposed model-based DL architecture demonstrat
 es that incorporating compression-aware beamforming significantly improves
  sum-rate performance\, even when the number of control bits is lower than
  the number of RIS elements.\n
DTSTART:20260605T140000Z
DTEND:20260605T160000Z
LOCATION:Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\, H
 3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Alexander Fernandes – Reconfigurable intelligent sur
 face-assisted wireless communication systems: signal processing techniques
  for channel estimation and phase shift compression
URL:/ece/channels/event/phd-defence-alexander-fernande
 s-reconfigurable-intelligent-surface-assisted-wireless-communication-37309
 0
END:VEVENT
END:VCALENDAR