Generalized Filtering with Transport Planning for Joint Modulation Conversion and Classification in AI-enabled Radios
Ali Krayani, Nobel J. William, Atm Shafiul Alam, Lucio Marcenaro, Zhijin Qin, Arumugam Nallanathan, Carlo S. Regazzoni · 2022
AI-empowered Cognitive Radio (i.e., AI-enabled radios) is a paradigm shift to achieve the highest level of Self-Awareness in future wireless communications. This work proposes a joint automatic modulation conversion and classification (AMCC) framework, which allows an AI-enabled wireless node to predict signals' dynamics of different modulation schemes and explain how it can be transported (converted) with minimal effort and forwarded with higher spectral efficiency. To achieve this goal, we propose a Generalized Filtering framework integrated by Transport Planning to learn the way of converting low-order modulations to high-order modulations, which has also been validated by performing the automatic modulation classification. Simulation results demonstrate the effective performance of our novel framework on converting and classifying multiple modulation formats.