人工智能和机器学习在5G技术中的应用进展综述

Alekya Nyalapelli, Shubham Sharma, Pranjal Phadnis, Maithili Patil, A. Tandle
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引用次数: 1

摘要

随着第五代(5G)无线通信在全球范围内的推广,概念化的用例和颠覆性的行业解决方案正在被部署,以提供流畅、无摩擦和安全的连接。人工智能(AI)和机器学习(ML)的前景可以被视为网络性能和管理复杂性自动化和优化的潜在驱动因素。网络行为的变化和复杂的现代应用带来了多样化的网络性能流量,服务提供商可以利用这些流量来满足网络需求并提供卓越的用户体验。现有的研究可以分为以下几个5G研究领域,包括网络流量、资源分配、网络切片、移动性管理、物理层安全等。本文的主要目标是为解决各种5G网络级问题的可行ml辅助解决方案的不断扩大的多样性提供全面的视角。本文最后深入调查了未来研究的挑战和未探索的方向,这些挑战和方向与使5G应用在未来用例中更加可靠有关。
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Recent Advancements in Applications of Artificial Intelligence and Machine Learning for 5G Technology: A Review
As the fifth generation (5G) of wireless communication rolls out worldwide, conceptualized use cases and disruptive industry solutions are being deployed to offer smooth, frictionless, and secure connectivity. The landscape of Artificial Intelligence (AI) and Machine Learning (ML) can be seen as potential drivers in the automation and optimization of network performances and management complexities. The shifting network behaviors and complicated modern applications present diverse network performance traffic, which can be exploited by service providers to deal with network demands and provide superior user experiences. The existing research can be divided into the following 5G research areas, which include network traffic, resource allocation, network slicing, mobility management, physical layer security, etc., to name a few. The primary objective of this paper is to provide a comprehensive perspective on the expanding diversity of viable ML-assisted solutions for tackling various 5G network-level issues. The paper concludes with an indepth investigation of the challenges and unexplored directions of future research pertaining to making 5G applications more reliable for future use cases.
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