{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/20678"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/20678","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"A learning-based scheme to optimise a cognitive handoff","abstract":"The evolution of communication standards promotes the development and use of several spectrum-sharing strategies. From the noted results, machine-learning techniques have paved a direction for radio protocols to achieve better levels of performance. With their definition, efficient learning practices and the use of effective spectrum sharing methods necessitate the development of better channel selection schemes. In this work, a radios' learning capability enables the manipulation of a spectrum-sharing concept. This involves the radio obeying certain rules in a spectrum sharing facility, which defines a decentralised form of coexistence (sharing) between the radios occupying that specific radio space. Amongst other benefits, the sharing promotes the node's independence in the radio space, between the cohabitating radios for the essence of efficient spectrum sharing. The learning dimension is realised by the use of a Stochastic Estimator Learning Automata (SELA) algorithm. It allows a radio node to roam independently, while achieving the goal of learning to control spectrum use over time. This is by selecting an effective action that defines the radio's channel choice, leading to the long-term benefit of learning the radio usage patterns. A key condition for spectrum sharing requires that a 'borrowed' channel be handed-over to the owner, in any network for the sake of fair sharing practices. The sharing practices promote the evolution of spectrum use by making use of a device called a Cognitive Radio (CR). The CR, as a device that is set to redefine the sharing landscape, creates a paradigm that will revolutionise the concept of machine learning in the communications world. For the CR to have a good level of functionality, the learning rate and evolution should be dynamic. This is because, the results from its interactions with other users enhances its capability of coexistence and further promotes the progression of the spectrum-sharing concept.","abstract_html":"The evolution of communication standards promotes the development and use of several spectrum-sharing strategies. From the noted results, machine-learning techniques have paved a direction for radio protocols to achieve better levels of performance. With their definition, efficient learning practices and the use of effective spectrum sharing methods necessitate the development of better channel selection schemes. In this work, a radios' learning capability enables the manipulation of a spectrum-sharing concept. This involves the radio obeying certain rules in a spectrum sharing facility, which defines a decentralised form of coexistence (sharing) between the radios occupying that specific radio space. Amongst other benefits, the sharing promotes the node's independence in the radio space, between the cohabitating radios for the essence of efficient spectrum sharing. The learning dimension is realised by the use of a Stochastic Estimator Learning Automata (SELA) algorithm. It allows a radio node to roam independently, while achieving the goal of learning to control spectrum use over time. This is by selecting an effective action that defines the radio's channel choice, leading to the long-term benefit of learning the radio usage patterns. A key condition for spectrum sharing requires that a 'borrowed' channel be handed-over to the owner, in any network for the sake of fair sharing practices. The sharing practices promote the evolution of spectrum use by making use of a device called a Cognitive Radio (CR). The CR, as a device that is set to redefine the sharing landscape, creates a paradigm that will revolutionise the concept of machine learning in the communications world. For the CR to have a good level of functionality, the learning rate and evolution should be dynamic. This is because, the results from its interactions with other users enhances its capability of coexistence and further promotes the progression of the spectrum-sharing concept.","abstract_has_math":false,"creators":["Gombiro, Kurai Luke"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Ventura, Neco"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-22T22:23:14Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/20678","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ventura, Neco"]},{"key":"dc:creator","label":"Author","values":["Gombiro, Kurai Luke"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-07-25T11:24:31Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-07-25T11:24:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2016"]},{"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/11427/20678"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The evolution of communication standards promotes the development and use of several spectrum-sharing strategies. From the noted results, machine-learning techniques have paved a direction for radio protocols to achieve better levels of performance. With their definition, efficient learning practices and the use of effective spectrum sharing methods necessitate the development of better channel selection schemes. In this work, a radios' learning capability enables the manipulation of a spectrum-sharing concept. This involves the radio obeying certain rules in a spectrum sharing facility, which defines a decentralised form of coexistence (sharing) between the radios occupying that specific radio space. Amongst other benefits, the sharing promotes the node's independence in the radio space, between the cohabitating radios for the essence of efficient spectrum sharing. The learning dimension is realised by the use of a Stochastic Estimator Learning Automata (SELA) algorithm. It allows a radio node to roam independently, while achieving the goal of learning to control spectrum use over time. This is by selecting an effective action that defines the radio's channel choice, leading to the long-term benefit of learning the radio usage patterns. A key condition for spectrum sharing requires that a 'borrowed' channel be handed-over to the owner, in any network for the sake of fair sharing practices. The sharing practices promote the evolution of spectrum use by making use of a device called a Cognitive Radio (CR). The CR, as a device that is set to redefine the sharing landscape, creates a paradigm that will revolutionise the concept of machine learning in the communications world. For the CR to have a good level of functionality, the learning rate and evolution should be dynamic. This is because, the results from its interactions with other users enhances its capability of coexistence and further promotes the progression of the spectrum-sharing concept."]},{"key":"dc:title","label":"Title","values":["A learning-based scheme to optimise a cognitive handoff"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ventura, Neco"],"dc:creator":["Gombiro, Kurai Luke"],"dc:date.accessioned":["2016-07-25T11:24:31Z"],"dc:date.available":["2016-07-25T11:24:31Z"],"dc:date.issued":["2016"],"dc:description.abstract":["The evolution of communication standards promotes the development and use of several spectrum-sharing strategies. From the noted results, machine-learning techniques have paved a direction for radio protocols to achieve better levels of performance. With their definition, efficient learning practices and the use of effective spectrum sharing methods necessitate the development of better channel selection schemes. In this work, a radios' learning capability enables the manipulation of a spectrum-sharing concept. This involves the radio obeying certain rules in a spectrum sharing facility, which defines a decentralised form of coexistence (sharing) between the radios occupying that specific radio space. Amongst other benefits, the sharing promotes the node's independence in the radio space, between the cohabitating radios for the essence of efficient spectrum sharing. The learning dimension is realised by the use of a Stochastic Estimator Learning Automata (SELA) algorithm. It allows a radio node to roam independently, while achieving the goal of learning to control spectrum use over time. This is by selecting an effective action that defines the radio's channel choice, leading to the long-term benefit of learning the radio usage patterns. A key condition for spectrum sharing requires that a 'borrowed' channel be handed-over to the owner, in any network for the sake of fair sharing practices. The sharing practices promote the evolution of spectrum use by making use of a device called a Cognitive Radio (CR). The CR, as a device that is set to redefine the sharing landscape, creates a paradigm that will revolutionise the concept of machine learning in the communications world. For the CR to have a good level of functionality, the learning rate and evolution should be dynamic. This is because, the results from its interactions with other users enhances its capability of coexistence and further promotes the progression of the spectrum-sharing concept."],"dc:identifier.uri":["http://hdl.handle.net/11427/20678"],"dc:language.iso":["eng"],"dc:publisher.department":["Department of Electrical Engineering"],"dc:publisher.institution":["University of Cape Town"],"dc:title":["A learning-based scheme to optimise a cognitive handoff"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MSc (Eng)"]},"updated_at":"2026-07-22T22:23:14Z"}