{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/80771"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/80771","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Resource Allocation for Quality of Service in a Heterogeneous Network","abstract":"\"Finally, we consider a wireless down-link scheduling problem. We study a scheduling rule, which we call the exponential rule, and prove that this rule is throughput-optimal, i.e., it makes the queues stable if there exists any rule which can do so. In the proof we use the fluid limit technique, along with a separation of time scales argument. Namely, the proof of the desired property of a \"\"conventional\"\" fluid limit involves a study of a different fluid limit on a \"\"finer\"\" time scale. Further, we show that in a heavy traffic limit, this rule minimizes for all times, the maximum (scaled) queue length (pathwise optimality). We next compare this rule to some other algorithms that have been proposed in the literature, and observe that the exponential rule compares favorably with them with regard to both packet delays and average throughput.\"","abstract_html":""Finally, we consider a wireless down-link scheduling problem. We study a scheduling rule, which we call the exponential rule, and prove that this rule is throughput-optimal, i.e., it makes the queues stable if there exists any rule which can do so. In the proof we use the fluid limit technique, along with a separation of time scales argument. Namely, the proof of the desired property of a ""conventional"" fluid limit involves a study of a different fluid limit on a ""finer"" time scale. Further, we show that in a heavy traffic limit, this rule minimizes for all times, the maximum (scaled) queue length (pathwise optimality). We next compare this rule to some other algorithms that have been proposed in the literature, and observe that the exponential rule compares favorably with them with regard to both packet delays and average throughput."","abstract_has_math":false,"creators":["Shakkottai, Sanjay Govindaraju"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Srikant, R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:08:05Z","date_published":"2015-09-25T20:08:05Z","updated_at":"2026-07-22T22:26:14Z","subjects":["Engineering, Electronics and Electrical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3044220"],"render_values":[{"text":"(MiAaPQ)AAI3044220","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/80771","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Srikant, R."]},{"key":"dc:creator","label":"Author","values":["Shakkottai, Sanjay Govindaraju"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:08:05Z","10000-01-01","2002"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Electronics and Electrical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/80771","(MiAaPQ)AAI3044220"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"Finally, we consider a wireless down-link scheduling problem. We study a scheduling rule, which we call the exponential rule, and prove that this rule is throughput-optimal, i.e., it makes the queues stable if there exists any rule which can do so. In the proof we use the fluid limit technique, along with a separation of time scales argument. Namely, the proof of the desired property of a \"\"conventional\"\" fluid limit involves a study of a different fluid limit on a \"\"finer\"\" time scale. Further, we show that in a heavy traffic limit, this rule minimizes for all times, the maximum (scaled) queue length (pathwise optimality). We next compare this rule to some other algorithms that have been proposed in the literature, and observe that the exponential rule compares favorably with them with regard to both packet delays and average throughput.\"","Made available in DSpace on 2015-09-25T20:08:05Z (GMT). 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We study a scheduling rule, which we call the exponential rule, and prove that this rule is throughput-optimal, i.e., it makes the queues stable if there exists any rule which can do so. In the proof we use the fluid limit technique, along with a separation of time scales argument. Namely, the proof of the desired property of a \"\"conventional\"\" fluid limit involves a study of a different fluid limit on a \"\"finer\"\" time scale. Further, we show that in a heavy traffic limit, this rule minimizes for all times, the maximum (scaled) queue length (pathwise optimality). We next compare this rule to some other algorithms that have been proposed in the literature, and observe that the exponential rule compares favorably with them with regard to both packet delays and average throughput.\"","Made available in DSpace on 2015-09-25T20:08:05Z (GMT). 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