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Spectrum Analysis and Prediction Using Long Short Term Memory Neural Networks and Cognitive Radios

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.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc1062877
Date12 1900
CreatorsHernandez Villapol, Jorge Luis
ContributorsVaranasi, Murali, Buckets, Bill, Namuduri, Kamesh
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatviii, 53 pages, Text
RightsPublic, Hernandez Villapol, Jorge Luis, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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