Vision-Assisted User Clustering for Robust mmWave-NOMA Systems

Aditya S. Rajasekaran, Hamza U. Sokun, Omar Maraqa, Halim Yanikomeroglu, Saad Al-Ahmadi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


When operated in the mmWave band, user channels get highly correlated which can be exploited in mmWave-NOMA systems to cluster a set of 'correlated' users together. Identifying the set of users to cluster greatly affects the viability of NOMA systems. Typically, only channel state information (CSI) is used to make these clustering decisions. When any problem arises in accessing up-to-date and accurate CSI, user clustering will not properly function due to its hard-dependency on CSI, and obviously, this will negatively affect the robustness of the NOMA systems. To improve the robustness of the NOMA systems, we propose to utilize emerging trends such as location-aware and camera-equipped base stations (CBSs) which do not require any extra radio frequency resource consumption. Specifically, we explore three different dimensions of feedback that a CBS can benefit from to solve the user clustering problem, namely CSI-based feedback and non-CSI-based feedback, comprised of user equipment (UE) location and the CBS camera feed. We first investigate how the vision assistance of a CBS can be used in conjunction with other dimensions of feedback to make clustering decisions in various scenarios. Later, we provide a simple user case study to illustrate how to implement vision-assisted user clustering in mmWave-NOMA systems to improve robustness, in which a deep learning (DL) beam selection algorithm is trained on the images captured by the CBS to perform NOMA clustering. We demonstrate that user clustering without CSI can achieve comparable performance to accurate CSI-based solutions, and user clustering can continue to function without much performance loss even in the scenarios where CSI is severely outdated or not available at all.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE Future Networks World Forum, FNWF 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665462501
StatePublished - 2022
Externally publishedYes
Event2022 IEEE Future Networks World Forum, FNWF 2022 - Virtual, Online, Canada
Duration: 12 Oct 202214 Oct 2022

Publication series

NameProceedings - 2022 IEEE Future Networks World Forum, FNWF 2022


Conference2022 IEEE Future Networks World Forum, FNWF 2022
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


  • Beamforming (BF)
  • Camera Base Station (CBS)
  • Deep Learning (DL)
  • Non-orthogonal multiple access (NOMA)
  • User Clustering

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Instrumentation


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