7 papers
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…
Finetune-Informed Pretraining Boosts Downstream Performance
Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan +2
Multimodal pretraining is effective for building general-purpose representations, but in many practical deployments, only one modality is heavily used during downstream fine-tuning…
Semi-Supervised Masked Autoencoders: Unlocking Vision Transformer Potential with Limited Data
Atik Faysal, Mohammad Rostami, Reihaneh Gh. Roshan +2
We address the challenge of training Vision Transformers (ViTs) when labeled data is scarce but unlabeled data is abundant. We propose Semi-Supervised Masked Autoencoder (SSMAE), a…
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…
DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches
Atik Faysal, Mohammad Rostami, Taha Boushine +3
We introduce DenoMAE2.0, an enhanced denoising masked autoencoder that integrates a local patch classification objective alongside traditional reconstruction loss to improve repres…
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…