Matches in SemOpenAlex for { <https://semopenalex.org/work/W4234176458> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4234176458 endingPage "496" @default.
- W4234176458 startingPage "491" @default.
- W4234176458 abstract "Cognitive Radio (CR) was introduced to improve the utilization of Radio Frequencies (RF) that remain under-utilized by the primary users (licensee). The main idea behind CR is to allow un-licensed (secondary) users to occupy vacancies in licensed bands. However, CR mandates the secondary user to vacate the frequency band within a specified time after the primary user attempts to use the frequency band. CR does not expect the primary users to share their frequency usage schedules and hence the secondary users have to scan and predict the vacancy. The advantage for the secondary users is that they do not pay for utilization of band, if they are conformal to the CR specifications. CR is the next generation of smart communication systems. CR requires continuous monitoring of the intended RF band in the intended geographical area. This information may be used to predict spectral vacancies (white spaces). Certain bands, e.g. Analog TV bands, will have pre declared utilization schedules but in general, spectrum utilization is a random process and hence prediction can be difficult. However, Deep Learning (DL) techniques can improve the accuracy of prediction. Deep Learning techniques require large and clean data sets to work correctly. Such data sets are also necessary to compare achievable accuracy of prediction algorithms. Towards this end, we have created data sets that can be used for simulation, training and testing of CR over GSM band (890-960MHz). A typical file with two hour of observations will have about 1.2 million samples. More than 1000 sets of data samples have been captured from urban and rural areas in India. All the data sets have been cleaned to avoid instrument errors and statistical outliers. In this paper we have used these standardized data sets to perform a comparative analysis of three DL methods for CR, viz. Auto-encoder (AE), Long Short-Term Memory (LSTM) and Multi Layer Perceptron (MLP). Results of the comparison are discussed." @default.
- W4234176458 created "2022-05-12" @default.
- W4234176458 date "2019-12-14" @default.
- W4234176458 modified "2023-09-26" @default.
- W4234176458 title "Deep Learning Predictive Models for Cognitive Radio System" @default.
- W4234176458 doi "https://doi.org/10.35940/ijitee.b1129.1292s19" @default.
- W4234176458 hasPublicationYear "2019" @default.
- W4234176458 type Work @default.
- W4234176458 citedByCount "1" @default.
- W4234176458 countsByYear W42341764582020 @default.
- W4234176458 crossrefType "journal-article" @default.
- W4234176458 hasBestOaLocation W42341764581 @default.
- W4234176458 hasConcept C108583219 @default.
- W4234176458 hasConcept C111919701 @default.
- W4234176458 hasConcept C119857082 @default.
- W4234176458 hasConcept C12267149 @default.
- W4234176458 hasConcept C124101348 @default.
- W4234176458 hasConcept C149946192 @default.
- W4234176458 hasConcept C154945302 @default.
- W4234176458 hasConcept C169258074 @default.
- W4234176458 hasConcept C2776257435 @default.
- W4234176458 hasConcept C2778116611 @default.
- W4234176458 hasConcept C31258907 @default.
- W4234176458 hasConcept C41008148 @default.
- W4234176458 hasConcept C555944384 @default.
- W4234176458 hasConcept C59201141 @default.
- W4234176458 hasConcept C74064498 @default.
- W4234176458 hasConcept C76155785 @default.
- W4234176458 hasConcept C92545706 @default.
- W4234176458 hasConcept C96391052 @default.
- W4234176458 hasConcept C98045186 @default.
- W4234176458 hasConceptScore W4234176458C108583219 @default.
- W4234176458 hasConceptScore W4234176458C111919701 @default.
- W4234176458 hasConceptScore W4234176458C119857082 @default.
- W4234176458 hasConceptScore W4234176458C12267149 @default.
- W4234176458 hasConceptScore W4234176458C124101348 @default.
- W4234176458 hasConceptScore W4234176458C149946192 @default.
- W4234176458 hasConceptScore W4234176458C154945302 @default.
- W4234176458 hasConceptScore W4234176458C169258074 @default.
- W4234176458 hasConceptScore W4234176458C2776257435 @default.
- W4234176458 hasConceptScore W4234176458C2778116611 @default.
- W4234176458 hasConceptScore W4234176458C31258907 @default.
- W4234176458 hasConceptScore W4234176458C41008148 @default.
- W4234176458 hasConceptScore W4234176458C555944384 @default.
- W4234176458 hasConceptScore W4234176458C59201141 @default.
- W4234176458 hasConceptScore W4234176458C74064498 @default.
- W4234176458 hasConceptScore W4234176458C76155785 @default.
- W4234176458 hasConceptScore W4234176458C92545706 @default.
- W4234176458 hasConceptScore W4234176458C96391052 @default.
- W4234176458 hasConceptScore W4234176458C98045186 @default.
- W4234176458 hasIssue "2S" @default.
- W4234176458 hasLocation W42341764581 @default.
- W4234176458 hasOpenAccess W4234176458 @default.
- W4234176458 hasPrimaryLocation W42341764581 @default.
- W4234176458 hasRelatedWork W11033076 @default.
- W4234176458 hasRelatedWork W11228924 @default.
- W4234176458 hasRelatedWork W12634471 @default.
- W4234176458 hasRelatedWork W13534744 @default.
- W4234176458 hasRelatedWork W14471487 @default.
- W4234176458 hasRelatedWork W1678066 @default.
- W4234176458 hasRelatedWork W3865299 @default.
- W4234176458 hasRelatedWork W4593459 @default.
- W4234176458 hasRelatedWork W4630997 @default.
- W4234176458 hasRelatedWork W8394581 @default.
- W4234176458 hasVolume "9" @default.
- W4234176458 isParatext "false" @default.
- W4234176458 isRetracted "false" @default.
- W4234176458 workType "article" @default.