发表机构
MIT Lincoln Laboratory（麻省理工学院林肯实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在自回归Transformer模型基础上添加FSQ分词器层，结合推理优化技术，在低延迟下提升了对OFDM数字电视干扰的抑制性能，通过PESQ指标验证优势并探索了相关应用场景。
AI 中文摘要
基于人工智能的结构化干扰抑制技术愈发普及，因为深度学习方法可通过联合考虑感兴趣信号（SOI）与信号混合体（SOI加干扰），表现优于传统方法。本研究在先前基于人工智能的方法（采用自回归Transformer模型）基础上，添加有限标量量化（FSQ）分词器层，旨在提升干扰抑制性能同时将整体延迟控制在最低。此外，我们还尝试其他推理优化技术，目标是在精度损失不大的前提下加快推理速度。我们开展实验探索该方向，实验中感兴趣信号为数字调制射频（RF）信号，结构化干扰为数字电视信号——一种极为常见的正交频分复用（OFDM）传输类型。结果显示，与传统技术及其他基于AI的方法的现有研究相比，本方法实现了低延迟和更高的干扰抑制效果。我们通过语音感知质量评估（PESQ）等音频指标证明了基于AI方法的优势，此外还探索了多种应用场景，并详细说明本干扰抑制算法如何在与操作相关的场景中使用。
英文摘要
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.
Comments7 pages, 10 figures, Accepted to the 2026 IEEE Military Communications Conference (MILCOM)
