ACO-Based Scheme in Edge Learning NOMA Networks for Task-Oriented Communications

被引:3
作者
Garcia, Carla E. [1 ]
Camana, Mario R. [1 ]
Koo, Insoo [2 ]
机构
[1] Univ Luxembourg, Interdisciplinary Ctr Secur Reliabil & Trust SnT, L-1855 Luxembourg, Luxembourg
[2] Univ Ulsan, Dept Elect Elect & Comp Engn, Ulsan 680749, South Korea
基金
新加坡国家研究基金会;
关键词
Task analysis; NOMA; Optimization; Servers; Resource management; Probability density function; Vectors; Wireless communication; Prediction methods; Source coding; Particle swarm optimization; Optimization methods; Simulation; Task-oriented communication; edge learning; non-orthogonal multiple access (NOMA); learning error; ant colony optimization (ACO); INTELLIGENCE;
D O I
10.1109/ACCESS.2024.3374635
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:37692 / 37701
页数:10
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