Vision Aided URLL Communications: Proactive Service Identification and Coexistence

Muhammad Alrabeiah, Umut Demirhan, Andrew Hredzak, Ahmed Alkhateeb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The support of coexisting Ultra-Reliable and Low-Latency Communication (URLLC) and enhanced Mobile Broad-Band (eMBB) services is a cornerstone challenge to wireless communication networks. Those two types of services introduce strict resource allocation requirements resulting in a power-struggle between reliability, latency, and resource utilization. The difficulty in addressing that challenge could be rooted in the predominant reactive approach to resource allocation in wireless networks, where the allocation operation is carried out based on received service requests and global network statistics. This paper proposes a novel framework termed service identification to develop proactive resource allocation algorithms. The framework is based on visual data and deep learning, and its objective is to equip future wireless networks with the ability to anticipate incoming services and perform proactive resource allocation. To demonstrate the potential of the framework, a wireless network scenario with two coexisting URLLC and eMBB services is considered, and a deep learning algorithm is designed to utilize RGB video frames and predict incoming service type and its request time. An evaluation dataset is developed and used to evaluate the performance of the algorithm. The results show that the algorithm enables a 78% utilization of idle network resources, which emphasizes the value of proaction.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages174-178
Number of pages5
ISBN (Electronic)9780738131269
DOIs
StatePublished - Nov 1 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2020-November
ISSN (Print)1058-6393

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period11/1/2011/5/20

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Vision Aided URLL Communications: Proactive Service Identification and Coexistence'. Together they form a unique fingerprint.

Cite this