Overview of Machine Learning Approaches for Wireless Communication

Author:

Ensari Tolga1,Günay Melike2,Nalçakan Yağız3,Yildiz Eyyüp4

Affiliation:

1. Istanbul University, Turkey

2. Istanbul Kultur University, Turkey

3. Altinbas University, Turkey

4. Erzincan University, Turkey

Abstract

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

Publisher

IGI Global

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Density-Based Machine Learning Scheme for Outlier Detection in Smart Forest Fire Monitoring Sensor Cloud;International Journal of Cloud Applications and Computing;2022-07-21

2. Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud;International Journal of Cloud Applications and Computing;2021-01

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