A space-efficient and multi-objective case-based reasoning in cognitive radio
Ken-Shin Huang, Chih-Hseng Lin, Pao‐Ann Hsiung · 2010
Cognitive Radios (CRs) are intelligent mobile systems that can adapt to changing network environments. Adaptivity is achieved through reasoning and learning from past experiences. The conventional case-based reasoning (CBR) method requires a large storage space for the cases; however, embedded system have limited storage space. This work proposes a novel CBR method for improving the storage space efficiency. The CBR method is based on the Divide-and-Conquer technique. Our experiments show that the method can reach higher accuracy with a maximum of 797% improvement.