Artificial neural network decoding of multi-h CPM

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dc.contributor.advisorBraun, Robin Men_ZA
dc.contributor.authorKannemeyer, Johan Etienneen_ZA
dc.date.accessioned2016-05-13T09:28:36Z
dc.date.available2016-05-13T09:28:36Z
dc.date.issued1997en_ZA
dc.description.abstractThe purpose of this report is to set out the results of an investigation into the artificial neural network (ANN) decoding of multi-h continuous phase modulation (CPM) schemes. Multi-h CPM schemes offer forward error correction (FEC) capabilities for continuous transmission, digital communication systems. Multi-h CPM is reported to be a bandwidth efficient alternative to other FEC techniques such as convolutional coding, while neural networks allow for high speed decoding. A neural network decoder was found in [12], where it had been used for the decoding of a convolutional code. This neural network structure by Xiao-an Wang and Stephen 'B. Wicker implements the Viterbi Algorithm (VA). All the necessary decoding information is contained in the interconnections of the ANN, and can be found by inspection of the state trellis diagram of the convolutional code. The decoder therefore requires no training. Since all the computation is done by analogue neurons and shift registers, the neural network reduces to a hybrid digital-analogue implementation of the VA. The use of analogue neurons allows the structure to be used for high data rate communications. Furthermore, the decoder is reported to be suitable for VLSI implementation.en_ZA
dc.identifier.apacitationKannemeyer, J. E. (1997). <i>Artificial neural network decoding of multi-h CPM</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/19638en_ZA
dc.identifier.chicagocitationKannemeyer, Johan Etienne. <i>"Artificial neural network decoding of multi-h CPM."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1997. http://hdl.handle.net/11427/19638en_ZA
dc.identifier.citationKannemeyer, J. 1997. Artificial neural network decoding of multi-h CPM. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Kannemeyer, Johan Etienne AB - The purpose of this report is to set out the results of an investigation into the artificial neural network (ANN) decoding of multi-h continuous phase modulation (CPM) schemes. Multi-h CPM schemes offer forward error correction (FEC) capabilities for continuous transmission, digital communication systems. Multi-h CPM is reported to be a bandwidth efficient alternative to other FEC techniques such as convolutional coding, while neural networks allow for high speed decoding. A neural network decoder was found in [12], where it had been used for the decoding of a convolutional code. This neural network structure by Xiao-an Wang and Stephen 'B. Wicker implements the Viterbi Algorithm (VA). All the necessary decoding information is contained in the interconnections of the ANN, and can be found by inspection of the state trellis diagram of the convolutional code. The decoder therefore requires no training. Since all the computation is done by analogue neurons and shift registers, the neural network reduces to a hybrid digital-analogue implementation of the VA. The use of analogue neurons allows the structure to be used for high data rate communications. Furthermore, the decoder is reported to be suitable for VLSI implementation. DA - 1997 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1997 T1 - Artificial neural network decoding of multi-h CPM TI - Artificial neural network decoding of multi-h CPM UR - http://hdl.handle.net/11427/19638 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/19638
dc.identifier.vancouvercitationKannemeyer JE. Artificial neural network decoding of multi-h CPM. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1997 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/19638en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleArtificial neural network decoding of multi-h CPMen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Eng)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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