A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks
This work addresses adaptation challenges in wireless communications, but it appears incremental as it builds on existing learning frameworks with specific enhancements.
The paper tackles the problem of machine learning frameworks' inability to adapt to dynamic wireless environments and tasks by proposing a unified cognitive learning framework inspired by primate brain mechanisms, demonstrating its advantages in modulation recognition using a public dataset.
Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals. However, their incapability of adapting to the dynamic wireless environment 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 the dynamic wireless environment 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 the dynamic environment 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.