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

arXiv: https://arxiv.org/abs/2106.00501 · HTML: https://mlsanity.com/paper/2106.00501/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Online Meta-Learning](https://mlsanity.com/paper/1902.08438/) (2019)
- [Meta Learning-based MIMO Detectors: Design, Simulation, and Experimental Test](https://mlsanity.com/paper/2012.04379/) (2020)

## Built on by (most cited first, 3 of 3)

- [RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks](https://mlsanity.com/paper/2403.16397/) (2024)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation](https://mlsanity.com/paper/2303.08546/) (2023)

## Abstract (arXiv)

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.