Machine Learning Approach for 5G Hybrid Technologies

A. Mathews, G. Glan Devadhas
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Abstract

The rapid evolution of mobile communication networks is due to the large increase in the number of users. But higher throughput is not the only criterion to address the fifth generation of cellular networks. It mainly focuses on the redressal of the possible issues of the networks. The mostly found issues are lesser area of coverage, non-linear signal effects and the dispersion which is found to occur during the signal pathway. This work entails on increasing the maximum limit of coverage without signal loss. Through the usage of microcells in the proposed system, maximum limit of coverage is achieved in highly populated areas. The simulations are carried out using software MATLAB 2017a and Opti-System, in which enhanced symbol error rate plot and reduced out of band emissions power performance have been improved. Finally, the conclusion and the future scope of the work has been discussed.
5G混合技术的机器学习方法
移动通信网络的快速发展是由于用户数量的大量增加。但更高的吞吐量并不是解决第五代蜂窝网络的唯一标准。它主要侧重于解决网络可能出现的问题。大多数发现的问题是较小的覆盖面积,非线性信号效应和发现在信号通路中发生的色散。这项工作需要在不丢失信号的情况下增加最大覆盖范围。通过在拟议的系统中使用微蜂窝,在人口密集的地区实现了最大的覆盖范围。利用MATLAB 2017a和Opti-System软件进行仿真,增强了符号误码率图,降低了带外发射功率性能。最后,对本文的结论和未来的工作范围进行了讨论。
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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