Edge Learning Optimization in Task-Oriented NOMA Communications for Autonomous Vehicle Perception

January 1, 2024·
Carla E Garcia
,
Mario R Camana
,
Abuzar B Mohammad
,
Jorge Querol
,
Symeon Chatzinotas
· 1 min read
Abstract
The Internet of Vehicles (IoV) is undergoing swift advancements in capability and intelligence, poised to facilitate a diverse range of innovative applications. Within the IoV, edge learning empowers intelligent applications and services by leveraging data -driven tasks. Therefore, in this paper, we propose optimizing the edge learning error prediction within an edge-supported Non-Orthogonal Multiple Access (NOMA) in task-oriented communications. Specifically, we consider three autonomous vehicle perception tasks, called: object detection, traffic sign, and weather classification. For this purpose, we propose a novel approach based on the Particle Swarm Optimization (PSO) algorithm to jointly minimize the edge learning error and optimize power allocation variables. Moreover, we investigate alternative benchmark schemes, including Quantum Particle Swarm Optimization, Cuckoo Search, and Butterfly Op-timization algorithms. Satisfactorily, our simulations substantiate the superiority of the PSO algorithm over the baseline schemes, delivering superior performance with reduced computation time.
Type
Publication
2024 IEEE Wireless Communications and Networking Conference (WCNC)
publication

Abstract & Overview

The Internet of Vehicles (IoV) is undergoing swift advancements in capability and intelligence, poised to facilitate a diverse range of innovative applications. Within the IoV, edge learning empowers intelligent applications and services by leveraging data -driven tasks. Therefore, in this paper, we propose optimizing the edge learning error prediction within an edge-supported Non-Orthogonal Multiple Access (NOMA) in task-oriented communications. Specifically, we consider three autonomous vehicle perception tasks, called: object detection, traffic sign, and weather classification. For this purpose, we propose a novel approach based on the Particle Swarm Optimization (PSO) algorithm to jointly minimize the edge learning error and optimize power allocation variables. Moreover, we investigate alternative benchmark schemes, including Quantum Particle Swarm Optimization, Cuckoo Search, and Butterfly Op-timization algorithms. Satisfactorily, our simulations substantiate the superiority of the PSO algorithm over the baseline schemes, delivering superior performance with reduced computation time.

Publication Metadata

  • Authors: Carla E Garcia, Mario R Camana, Abuzar B Mohammad, Jorge Querol, Symeon Chatzinotas
  • Venue: 2024 IEEE Wireless Communications and Networking Conference (WCNC) (2024)
  • Research Areas: Satellite Communications, Signal Processing
  • DOI: 10.1109/wcnc57260.2024.10570514
Satellite Communications Signal Processing