A Unified Cognitive Learning Framework for Adapting to Dynamic Environments and Tasks
Qihui Wu, Tianchen Ruan, Fuhui Zhou, Yang Huang, Fan Xu, Shijin Zhao, Ya Liu, Xuyang Huang
Abstract
Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals. However, their incapability of adapting to dynamic wireless environments and tasks and of self-learning limit their extensive applications and achievable performance. Inspired by the great flexibility and adaptation of primate behaviors due to the brain cognitive mechanism, a unified cognitive learning (CL) framework is proposed for dynamic wireless environments and tasks. The mathematical framework for our proposed CL is established. Using the public and authoritative dataset, we demonstrate that our proposed CL framework has three advantages, namely, the capability of adapting to dynamic environments and tasks, the self-learning capability and the capability of “good money driving out bad money” by taking modulation recognition as an example. The proposed CL framework can enrich the current learning frameworks and widen the applications.