用机器联觉思想优化太赫兹通信感知一体化，提升空地网络效率
Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network
- 借鉴机器联觉思想融合多模态信号，挖掘太赫兹硬件与信道自由度
- 实现三维动态通信感知链路，降低系统延迟并显著提升性能
- 适合空地网络、智能感知与太赫兹通信研究者参考
太赫兹频段的通信感知一体化（ISAC）对未来的空地网络至关重要，但其独特的传播特性与硬件限制在提升性能的同时增加了操作延迟。本文提出一种受机器联觉（SoM）启发的多模态感知融合框架，以增强太赫兹频段的ISAC传输。通过利用太赫兹硬件与信道中的固有自由度，该框架优化了射频环境。设计了考虑俯仰角偏差（squint-aware）的波束管理机制，提升了空地网络的适应性，支持三维动态ISAC链路。融合视觉数据可快速定位用户与目标，结合定制化的多模态学习算法优化混合预编码器。引入新评估指标进行综合性能分析，大量实验表明，所提方案显著提升了ISAC效率。
原文摘要 · Abstract (English)
Integrated sensing and communication (ISAC) within sub-THz frequencies is crucial for future air-ground networks, but unique propagation characteristics and hardware limitations present challenges in optimizing ISAC performance while increasing operational latency. This paper introduces a multi-modal sensing fusion framework inspired by synesthesia of machine (SoM) to enhance sub-THz ISAC transmission. By exploiting inherent degrees of freedom in sub-THz hardware and channels, the framework optimizes the radio-frequency environment. Squint-aware beam management is developed to improve air-ground network adaptability, enabling three-dimensional dynamic ISAC links. Leveraging multi-modal information, the framework enhances ISAC performance and reduces latency. Visual data rapidly localizes users and targets, while a customized multi-modal learning algorithm optimizes the hybrid precoder. A new metric provides comprehensive performance evaluation, and extensive experiments demonstrate that the proposed scheme significantly improves ISAC efficiency.
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