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Virginia Tech

Scalable Parameter Management using Casebased Reasoning for Cognitive Radio Applications

Abstract

dc:description.abstract

Cognitive radios have applied various forms of artificial intelligence (AI) to wireless systems in order to solve the complex problems presented by proper link management, network traffic balance, and system efficiency. Casebased reasoning (CBR) has seen attention as a prospective avenue for storing and organizing past information in order to allow the cognitive engine to learn from previous experience. CBR uses past information and observed outcomes to form empirical relationships that may be difficult to model apriori. As wireless systems become more complex and more tightly time constrained, scalability becomes an apparent concern to store large amounts of information over multiple dimensions. This thesis presents a renewed look at an abstract application of CBR to CR. By appropriately designing a case structure with useful information both to the cognitive entity as well as the underlying similarity relationships between cases, an accurate problem description can be developed and indexed. By separating the components of a case from the parameters that are meaningful to similarity, the situation can be quickly identified and queried given proper design. A data structure with this in mind is presented that orders cases in terms of general placement in Euclidean space, but does not require the discrete calculation of distance between the query case and all cases stored. By grouping possible similarity dimension values into distinct partitions called "similarity buckets", a data structure is developed with constant (O(1)) access time, which is an improvement of several orders of magnitude over traditional linear approaches (O(n)).

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Electrical and Computer Engineering
Department dc:contributor.department
Electrical and Computer Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2012

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Ali, Daniel Ray
Chair dc:contributor.committeechair
  • Park, Jung-Min Jerry
Committee members dc:contributor.committeemember
  • Amanna, Ashwin E.
  • Clancy, Thomas Charles III

Subjects

dc:subject × 4

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
etd-05032012-140854
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/32167

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Ali, Daniel Ray. Scalable Parameter Management using Casebased Reasoning for Cognitive Radio Applications. masters thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/32167