AI-based structured interference rejection has grown more popular because deep learning approaches can outperform traditional methods by jointly considering the signal of interest (SOI) and the signal mixture (SOI plus interference). This work builds on a previous AI-enabled approach utilizing autoregressive transformer-based models by adding a Finite Scalar Quantization (FSQ) tokenizer layer which aims to improve the interference rejection performance while keeping overall latency to a minimum. Additionally, we experiment with other inference optimization techniques with the goal of speeding up inference without much accuracy loss. We explore this space with an experiment where the SOI is a digitally modulated radio frequency (RF) signal and the structured interference is a digital television signal, an extremely common type of Orthogonal Frequency-Division Multiplexing (OFDM) transmission. Our results achieve low latency and increased interference rejection over traditional techniques and prior work with other AI-enabled methods. We demonstrate the benefits of the AI-enabled approaches via audio metrics such as Perceptual Evaluation of Speech Quality (PESQ). Additionally, we explore a variety of applications and detail how our interference rejection algorithm may be used in operationally-relevant scenarios.
Figures & tables
Fig. 1: A real-time resilient communications use case. The blue gear box represents the AI model doing the processing on the receive end of the communications link.
Fig. 2: Adapted from [ 1 ] . Architecture diagram for the RF Finite Scalar Quantization (FSQ) tokenizer.
Fig. 3: Adapted from [ 1 , 3 ] . Architecture diagram for the RF Transformer using the RF FSQ tokenizer.
Fig. 4: Adapted from [ 3 ] . Data preparation process for the training pipeline.
Fig. 5: Audio quality evaluation for digital radio experiment. Metrics include Perceptual Evaluation of Speech Quality (PESQ), Signal to Distortion ratio (SDR), Log-Spectral Distance (LSD), and Mel-Cepstral Distance (Mel-CD).
Fig. 6: Audio intelligibility evaluation for digital radio experiment. Metrics include Short-Time Objective Intelligibility (STOI) and extended STOI (eSTOI).
Fig. 7: Overall latencies for the RF WaveNet, RF Transformer Decoder, and RF Transformer + Tokenizer models on the NVIDIA Jetson AGX Orin (left column) and NVIDIA H100 NVL (right column) evaluated for FP32. Note that all three models have good or fair latency for real-time streaming applications on the Jetson.
Fig. 8: Output throughput for the RF WaveNet, RF Transformer Decoder, and RF Transformer + Tokenizer models on the NVIDIA Jetson AGX Orin (left column) and NVIDIA H100 NVL (right column) evaluated for FP32. Note that all three models are currently unacceptable for real-time streaming applications on the Jetson, but the Transformer + Tokenizer had very poor throughput in particular thus no points are shown in the range of the plot axes for Jetson.
Fig. 9: Optimized latency and output throughput for the RF Transformer Decoder NVIDIA Jetson AGX Orin using the Torch-TensorRT library at FP16 precision.
Fig. 10: Spectrum sharing use case between radar and a structured interference source.