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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1062877 |
| Date | 12 1900 |
| Creators | Hernandez Villapol, Jorge Luis |
| Contributors | Varanasi, Murali, Buckets, Bill, Namuduri, Kamesh |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | viii, 53 pages, Text |
| Rights | Public, Hernandez Villapol, Jorge Luis, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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