通过语义雷达中心ISAC实现应急通信
Enabling Emergency Communication via Semantic Radar-Centric ISAC
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中文总结 AI 辅助
针对战场通信中断问题，提出语义雷达中心ISAC框架，利用向量量化语义编码器压缩信息，在保持雷达感知功能的同时实现应急通信。
中文摘要 AI 辅助
作战飞机和其他现代军事平台配备了两个共址的射频资产：一个用于感知的雷达和一个用于通信的专用无线电。然而，在有争议的战场环境中，专用通信路径极有可能受到干扰、阻塞或物理损坏的攻击，而雷达仍能完全运行。一个自然的后备方案是将幸存的雷达孔径重新用作应急通信信道。困难在于，雷达是专门为感知而非通信而设计的，因此将任何信息加载到其波形上都会扰动波束方向图，并不可避免地削弱感知性能：我们推送的比特越多，损失的感知能力就越多。我们将核心问题确定为：在保持雷达主要感知功能的同时，通过以雷达为中心的集成感知与通信（ISAC）链路实现应急通信。因此，我们提出了一种语义雷达中心ISAC框架，该框架将学习到的向量量化语义编码器（将任务相关内容压缩为几十到几百比特）与限制在保守的空间-频谱配置子集（该子集尊重有界的感知退化预算）内的宽带相控阵MIMO雷达配对。在SAR目标分类和文本分类上的实验表明，我们的框架在保持雷达波束方向图几乎不变的同时，为下游任务支持可靠的应急通信。
英文摘要
Combat aircraft and other modern military platforms field two co-located RF assets: a radar for sensing and a dedicated radio for communication. In contested battlefield environments, however, the dedicated communication path is highly likely to be targeted and disrupted by jamming, blockage, or physical damage, while the radar remains fully operational. A natural fallback is to repurpose the surviving radar aperture as an emergency communication channel. The difficulty is that radar is purposely engineered for sensing rather than communication, so loading any information onto its waveform perturbs the beampattern and inevitably erodes sensing performance: the more bits we push, the more sensing we lose. We identify the core problem as enabling emergency communication via a radar-centric integrated sensing and communication (ISAC) link while preserving the radar's primary sensing function. Therefore, we propose a semantic radar-centric ISAC framework that pairs a learned vector-quantized semantic encoder, which compresses task-relevant content into a few tens to a few hundred bits, with a wideband phased-MIMO radar restricted to a conservative spatial-spectral configuration subset that respects a bounded sensing-degradation budget. Experiments on SAR target classification and text classification show that our framework supports reliable emergency communication for downstream tasks while keeping the radar beampattern almost unchanged.
发表机构
- Virginia Tech（弗吉尼亚理工大学）
- George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。
