Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

Big Data-Based Spectrum Sensing for Cognitive Radio Networks Using Artificial Intelligence

Suriya Murugan, Sumithra M. G.
Copyright: © 2020 |Pages: 14
DOI: 10.4018/978-1-5225-9750-6.ch009
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Abstract

Cognitive radio has emerged as a promising candidate solution to improve spectrum utilization in next generation wireless networks. Spectrum sensing is one of the main challenges encountered by cognitive radio and the application of big data is a powerful way to solve various problems. However, for the increasingly tense spectrum resources, the prediction of cognitive radio based on big data is an inevitable trend. The signal data from various sources is analyzed using the big data cognitive radio framework and efficient data analytics can be performed using different types of machine learning techniques. This chapter analyses the process of spectrum sensing in cognitive radio, the challenges to process spectrum data and need for dynamic machine learning algorithms in decision making process.
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Literature Survey

In Huang, Wang, Bai et al. (2018) describe the standardization process of the fifth generation (5G) of wireless communications has recently been accelerated. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. Authors investigated various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modelling and proposed a big data and machine learning enabled wireless channel model framework.

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