{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/19638"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/19638","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Artificial neural network decoding of multi-h CPM","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Kannemeyer, Johan Etienne"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Braun, Robin M"],"committee_chairs":[],"committee_members":[],"year":1997,"date_issued":"1997","date_published":"1997","updated_at":"2026-07-22T22:23:05Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/19638","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Braun, Robin M"]},{"key":"dc:creator","label":"Author","values":["Kannemeyer, Johan Etienne"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-05-13T09:28:36Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-05-13T09:28:36Z"]},{"key":"dc:date.issued","label":"Date","values":["1997"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc (Eng)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/19638"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Artificial neural network decoding of multi-h CPM"]}]}],"canonical_facts":{"dc:contributor.advisor":["Braun, Robin M"],"dc:creator":["Kannemeyer, Johan Etienne"],"dc:date.accessioned":["2016-05-13T09:28:36Z"],"dc:date.available":["2016-05-13T09:28:36Z"],"dc:date.issued":["1997"],"dc:description.abstract":["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. 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