3 papers
cs.LG2026
Large Language Models Can Perform Automatic Modulation Classification via Discretized Self-supervised Candidate Retrieval
Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan +3
Identifying wireless modulation schemes is essential for cognitive radio, but standard supervised models often degrade under distribution shift, and training domain-specific wirele…
cs.LG2025
Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs
Mohammad Rostami, Atik Faysal, Reihaneh Gh. Roshan +3
Automatic Modulation Classification (AMC) is critical for efficient spectrum management and robust wireless communications. However, AMC remains challenging due to the complex inte…
cs.LG2025
DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals
Atik Faysal, Taha Boushine, Mohammad Rostami +5
We propose Denoising Masked Autoencoder (Deno-MAE), a novel multimodal autoencoder framework for denoising modulation signals during pretraining. DenoMAE extends the concept of mas…