Emerging Technologies in Software, Hardware, and Management Aspects Toward the 5G Era: Trends and Challenges

I. Belikaidis, A. Georgakopoulos, E. Kosmatos, S. Vassaki, Orestis-Andreas Liakopoulos, V. Foteinos, P. Vlacheas, P. Demestichas
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引用次数: 2

Abstract

5G networks would be a heterogeneous networks (HetNets), meaning that different networks will be integrated all together to a unified system, enabling aggregation of multiple existing radio access technologies (RATs) such as LTE‐A, Wi‐Fi, D2D, and even lightly licensed. 5G will be designed to be a viable, robust, and scalable technology. Another positive result with the introduction of 5G communication systems will be the drastic energy consumption reduction and energy harvesting that will help the industry to have an astounding usage growth. This chapter elaborates on the main 5G requirements and presents the status and challenges in hardware and software development. It focuses on the status and challenges in 5G wireless communications by focusing on physical layer, MAC and radio resource management (RRM). The chapter investigates the benefits of machine learning in 5G network management. It describes the use of various machine learning mechanisms for the service classification problem and also investigates the performance of different algorithms.
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面向5G时代的软件、硬件和管理方面的新兴技术:趋势与挑战
5G网络将是一个异构网络(HetNets),这意味着不同的网络将被集成到一个统一的系统中,从而实现多种现有无线接入技术(rat)的聚合,如LTE‐a、Wi‐Fi、D2D,甚至是少量许可。5G将被设计成一种可行、强大和可扩展的技术。引入5G通信系统的另一个积极成果将是大幅降低能耗和能量收集,这将有助于该行业实现惊人的使用增长。本章阐述了5G的主要需求,并介绍了硬件和软件开发的现状和挑战。重点从物理层、MAC和无线电资源管理(RRM)三个方面分析5G无线通信的现状和挑战。本章探讨了机器学习在5G网络管理中的好处。它描述了用于服务分类问题的各种机器学习机制的使用,并研究了不同算法的性能。
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