Article (Scientific journals)
ACO-based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications
GARCIA MORETA, Carla Estefania; Camana, Mario R.; Koo, Insoo
2024In IEEE Access, p. 1-1
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Keywords :
task-oriented communications; edge learning AI; artificial intelligence; non-orthogonal multiple access; ant colony optimization
Abstract :
[en] Conventional communications systems centered on data prioritize maximizing network throughput using Shannon’s theory, which is primarily concerned with securely transmitting the data despite limited radio resources. However, in the realm of edge learning, these methods frequently fall short because they depend on traditional source coding and channel coding principles, ultimately failing to improve learning performance. Consequently, it is crucial to transition from a data-centric viewpoint to a task-oriented communications approach in wireless system design. Therefore, in this paper, we propose efficient communications under a task-oriented principle by optimizing power allocation and edge learning-error prediction in an edge-aided non-orthogonal multiple access (NOMA) network. Furthermore, we propose a novel approach based on the ant colony optimization (ACO) algorithm to jointly minimize the learning error and optimize the power allocation variables. Moreover, we investigate four additional benchmark schemes (particle swarm optimization, quantum particle swarm optimization, cuckoo search, and butterfly optimization algorithms). Satisfactorily, simulation results validate the superiority of the ACO algorithm over the baseline schemes, achieving the best performance with less computation time. In addition, the integration of NOMA in the proposed task-oriented edge learning system obtains higher sum rate values than those achieved by conventional schemes.
Disciplines :
Computer science
Author, co-author :
GARCIA MORETA, Carla Estefania  ;  University of Luxembourg
Camana, Mario R. ;  Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg
Koo, Insoo ;  Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea
External co-authors :
yes
Language :
English
Title :
ACO-based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications
Publication date :
07 March 2024
Journal title :
IEEE Access
ISSN :
2169-3536
Publisher :
Institute of Electrical and Electronics Engineers (IEEE)
Pages :
1-1
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
Development Goals :
9. Industry, innovation and infrastructure
FnR Project :
NRF-2021R1A2B5B01001721
Funders :
Regional Innovation Strategy
Ministry of Science and ICT, South Korea
Funding number :
NRF-2021R1A2B5B01001721
Funding text :
Korean Government Ministry of Science and ICT (MSIT)
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