8th International Conference on Cognitive Radio Oriented Wireless Networks

Research Article

A Feature Partitioning Approach to Casebased Reasoning in Cognitive Radios

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  • @INPROCEEDINGS{10.4108/icst.crowncom.2013.252035,
        author={Daniel Ali and Jung-Min Park and Ashwin Amanna},
        title={A Feature Partitioning Approach to Casebased Reasoning in Cognitive Radios},
        proceedings={8th International Conference on Cognitive Radio Oriented Wireless Networks},
        publisher={ICST},
        proceedings_a={CROWNCOM},
        year={2013},
        month={11},
        keywords={cognitive radios case-based reasoning data structure access time},
        doi={10.4108/icst.crowncom.2013.252035}
    }
    
  • Daniel Ali
    Jung-Min Park
    Ashwin Amanna
    Year: 2013
    A Feature Partitioning Approach to Casebased Reasoning in Cognitive Radios
    CROWNCOM
    IEEE
    DOI: 10.4108/icst.crowncom.2013.252035
Daniel Ali1,*, Jung-Min Park1, Ashwin Amanna1
  • 1: Virginia Tech
*Contact email: danielali@vt.edu

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. Case-based 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 outcome to form empirical relationships that may be difficult to model using theory. 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 paper presents a quickly accessible data structure designed to reduce access time several orders of magnitude as opposed to traditional similarity calculation methods. A framework is presented for case representation, which provides the core of useful information contained within a case. By grouping possible similarity dimension values into distinct partitions called buckets, we develop a data structure with constant O(1) access time.