A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks

Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals. 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. ACHINE learning (ML) has received an increasing attention and made great development in wireless communications [1]. It enables wireless communication systems to automatically learn and improve performance from experience without being explicitly programmed [2]. Moreover, a proper set of models and parameters is of great importance for the traditional ML algorithms. In this case, once the training is complete, the model and parameters are determined, and the ML algorithms can only perform the function that is trained.