AI-enhanced RF interference rejection uses transformers for faster, clearer transmissions

Researchers have developed an AI-enhanced method for rejecting radio frequency interference, outperforming traditional techniques by training on both the desired signal and interference mixtures. The new approach utilizes Autoregressive Transformer Decoder models, offering significantly faster inference speeds compared to previous WaveNet models. This method was demonstrated effectively in suppressing an OFDM interferer from an analog FM signal, making unintelligible transmissions clear and maintaining low latency on lightweight GPUs, with potential applications in national security and commercial sectors. AI

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IMPACT Potential for improved communication clarity in tactical and commercial RF environments using transformer-based AI.

RANK_REASON Academic paper detailing a new AI-based method for RF interference rejection.

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• arXiv
• Autoregressive Transformer Decoder
• WaveNet
• OFDM
• Jetson AGX Orin
• AI

• paper
• other