MA2CL: Multi-Agent Actor-Critic Learning Scheme for Efficient Resource Management in 5G-Enabled NB-IoT Networks

IF 0.9 Q4 TELECOMMUNICATIONS

Internet Technology Letters Pub Date : 2025-03-20 DOI:10.1002/itl2.70011

Sadhvi Parashar, Rajeev Arya

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引用次数: 0

Abstract

The allocation of spectrum resources for the future 5G-enabled Narrowband Internet of Things (NB-IoT) is one of the most critical issues that need to be resolved. Due to the massive amount of data that will be generated by the IoT, the need for efficient allocation of resources is also immense. This paper presents a novel interference model for managing the allocation of resources and avoiding overlapping interference in the 5G-enabled NB-IoT Networks. It adopts Reinforcement Learning (RL)-based algorithms to improve the network throughput and prevent overlapping interference. The proposed method utilizes a Multi-Agent Actor-Critic Learning (MA²CL) algorithm, which can improve the efficiency of the network. The simulation result illustrates the prominent enhancement in the throughput and spectral efficiency of the network. The performances of the proposed algorithm have been compared with benchmark schemes and achieved a 33.3% increase in network throughput and a 26.67% boost in spectral efficiency, respectively. The proposed work for efficient NB-IoT resource management may be suitable in industrial automation and intelligent transportation systems.

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MA2CL：支持5g的NB-IoT网络中高效资源管理的多智能体actor - critical学习方案

未来5g窄带物联网（NB-IoT）的频谱资源分配是需要解决的最关键问题之一。由于物联网将产生大量数据，因此对有效分配资源的需求也是巨大的。本文提出了一种新的干扰模型，用于在支持5g的NB-IoT网络中管理资源分配和避免重叠干扰。它采用基于强化学习（RL）的算法来提高网络吞吐量并防止重叠干扰。该方法采用了MA2CL （Multi-Agent Actor-Critic Learning）算法，提高了网络的效率。仿真结果表明，该网络在吞吐量和频谱效率方面有显著提高。通过与基准方案的性能比较，该算法的网络吞吐量和频谱效率分别提高了33.3%和26.67%。提出的高效NB-IoT资源管理工作可能适用于工业自动化和智能交通系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Internet Technology Letters

CiteScore

3.10

自引率

0.00%

发文量