Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction

Dynamic Spectrum Access (DSA) solutions equipped with spectrum prediction can enable proactive spectrum management and tackle the increasing demand for radio frequency (RF) bandwidth. Among various prediction techniques, Long Short-Term Memory (LSTM) is a deep learning model that has demonstrated hi...

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Autores principales:	Niranjana Radhakrishnan, Sithamparanathan Kandeepan, Xinghuo Yu, Gianmarco Baldini
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Spectrum prediction long short-term memory probability of error performance modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b521fd391a034bc0be1802b8b0cfa1c3
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id	oai:doaj.org-article:b521fd391a034bc0be1802b8b0cfa1c3
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spelling	oai:doaj.org-article:b521fd391a034bc0be1802b8b0cfa1c32021-11-18T00:01:41ZPerformance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction2169-353610.1109/ACCESS.2021.3125725https://doaj.org/article/b521fd391a034bc0be1802b8b0cfa1c32021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605607/https://doaj.org/toc/2169-3536Dynamic Spectrum Access (DSA) solutions equipped with spectrum prediction can enable proactive spectrum management and tackle the increasing demand for radio frequency (RF) bandwidth. Among various prediction techniques, Long Short-Term Memory (LSTM) is a deep learning model that has demonstrated high performance in forecasting spectrum characteristics. Although well-performing, the theoretical characterization of LSTM prediction performance has not been well developed in the literature. Therefore, in this article, we examine an LSTM based temporal spectrum prediction model and characterize its prediction performance through theoretical analysis. To this end, we analyze the LSTM prediction outputs over simulated Markov-model-based spectrum data and spectrum measurements data. Our results suggest that the predicted scores of the LSTM based system model can be described using mixtures of truncated Gaussian distributions. We also estimate the performance metrics using the mixture model and compare the results with the observed prediction performance over simulated and measured datasets.Niranjana RadhakrishnanSithamparanathan KandeepanXinghuo YuGianmarco BaldiniIEEEarticleSpectrum predictionlong short-term memoryprobability of errorperformance modelingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149582-149595 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Spectrum prediction long short-term memory probability of error performance modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Spectrum prediction long short-term memory probability of error performance modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971 Niranjana Radhakrishnan Sithamparanathan Kandeepan Xinghuo Yu Gianmarco Baldini Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction
description	Dynamic Spectrum Access (DSA) solutions equipped with spectrum prediction can enable proactive spectrum management and tackle the increasing demand for radio frequency (RF) bandwidth. Among various prediction techniques, Long Short-Term Memory (LSTM) is a deep learning model that has demonstrated high performance in forecasting spectrum characteristics. Although well-performing, the theoretical characterization of LSTM prediction performance has not been well developed in the literature. Therefore, in this article, we examine an LSTM based temporal spectrum prediction model and characterize its prediction performance through theoretical analysis. To this end, we analyze the LSTM prediction outputs over simulated Markov-model-based spectrum data and spectrum measurements data. Our results suggest that the predicted scores of the LSTM based system model can be described using mixtures of truncated Gaussian distributions. We also estimate the performance metrics using the mixture model and compare the results with the observed prediction performance over simulated and measured datasets.
format	article
author	Niranjana Radhakrishnan Sithamparanathan Kandeepan Xinghuo Yu Gianmarco Baldini
author_facet	Niranjana Radhakrishnan Sithamparanathan Kandeepan Xinghuo Yu Gianmarco Baldini
author_sort	Niranjana Radhakrishnan
title	Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction
title_short	Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction
title_full	Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction
title_fullStr	Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction
title_full_unstemmed	Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction
title_sort	performance analysis of long short-term memory-based markovian spectrum prediction
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b521fd391a034bc0be1802b8b0cfa1c3
work_keys_str_mv	AT niranjanaradhakrishnan performanceanalysisoflongshorttermmemorybasedmarkovianspectrumprediction AT sithamparanathankandeepan performanceanalysisoflongshorttermmemorybasedmarkovianspectrumprediction AT xinghuoyu performanceanalysisoflongshorttermmemorybasedmarkovianspectrumprediction AT gianmarcobaldini performanceanalysisoflongshorttermmemorybasedmarkovianspectrumprediction
_version_	1718425246052122624

Performance Analysis of Long Short-Term Memory-Based Markovian Spectrum Prediction

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