Spectrum Analysis and Prediction Using Long Short-Term Memory Neural Networks (LSTMs) and Cognitive Radios

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One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time ... continued below

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Hernandez Villapol, Jorge Luis December 2017.

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  • Hernandez Villapol, Jorge Luis

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One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.

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  • December 2017

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  • Jan. 27, 2018, 7:36 a.m.

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Hernandez Villapol, Jorge Luis. Spectrum Analysis and Prediction Using Long Short-Term Memory Neural Networks (LSTMs) and Cognitive Radios, thesis, December 2017; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc1062877/: accessed October 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .