用AI模型提升无线信号干扰抑制，兼顾低延迟与高精度。
Clearing the Underbrush: AI-Enhanced RF Interference Suppression
- 引入FSQ量化编码层，优化变压器模型的干扰识别能力。
- 在数字电视干扰下，干扰抑制性能优于传统方法和已有AI方案。
- 适合实时通信系统，如5G/6G基站或智能终端的信号处理场景。
基于人工智能的结构化干扰抑制方法因深度学习能联合考虑目标信号（SOI）与混合信号（SOI加干扰）而日益流行。本文在先前基于自回归变压器模型的AI方法基础上，引入有限标量量化（FSQ）分词层，旨在提升干扰抑制性能的同时保持最低延迟。此外，还探索多种推理优化技术，在不显著损失准确率的前提下加速推理过程。实验中，目标信号为数字调制射频信号，结构性干扰为常见正交频分复用（OFDM）传输的数字电视信号。结果表明，该方法在保持低延迟的同时，相比传统技术及现有其他AI方法，显著提升了干扰抑制能力。通过语音质量评估指标（如PESQ）验证了AI方法的优势，并探讨了其在实际运行场景中的多种应用潜力。
原文摘要 · Abstract (English)
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.
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