A feature partitioning approach to casebased reasoning in cognitive radios

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.