  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.
