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Published in Chapter:
An Adaptive Reasoning and Learning Framework for Mobile Cognitive Radio Systems
Chih-Sheng Lin (National Chung Cheng University, Taiwan, R.O.C.), Ken-Shin Huang (National Chung Cheng University, Taiwan, R.O.C.), Jih-Sheng Shen (National Chung Cheng University, Taiwan, R.O.C.), Shen-Yang Pan (National Chung Cheng University, Taiwan, R.O.C.), Shih-Shen Lu (National Chung Cheng University, Taiwan, R.O.C.), Wei-Wen Lin (National Chung Cheng University, Taiwan, R.O.C.), Pao-Ann Hsiung (National Chung Cheng University, Taiwan, R.O.C.), Mao-Hsu Yen (National Taiwan Ocean University, Taiwan, R.O.C.), Chu Yu (National Ilan University, Taiwan, R.O.C.), Sao-Jie Chen (National Taiwan University, Taiwan, R.O.C.), and William Cheng-Chung Chu (Tunghai University, Taiwan, R.O.C.)
Copyright: © 2012
|Pages: 18
DOI: 10.4018/978-1-61520-655-1.ch021
Abstract
Cognitive radios are intelligent mobile systems with self-adaptivity. Existing frameworks mainly focus on the radio aspects of system designs such as dynamic spectrum access and reduction of bit error rate. However, besides the radio aspects, cognitive radios also leverage other environmental data such as GPS location, system time, and user preferences. The authors propose an adaptive reasoning and learning framework (ARALF) for cognitive radio systems such that this gap between spectrum data and other environment data is bridged. The framework has a novel reasoning and learning mechanism that combines case-based reasoning and rule-based reasoning. Adaptivity and mobility are seamlessly blended into the framework so that users of cognitive radios are completely unaware of unexpected jitters due to environment changes.