Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network

TL;DR

This paper proposes a novel synesthesia-inspired multi-modal sensing framework to enhance sub-THz ISAC transmission in air-ground networks, improving performance and reducing latency through innovative beam management and learning algorithms.

Contribution

It introduces a synesthesia-inspired multi-modal sensing fusion framework that exploits hardware and channel degrees of freedom to optimize sub-THz ISAC performance in air-ground networks.

Findings

Significant improvement in ISAC efficiency demonstrated

Enhanced 3D dynamic ISAC link adaptability

Reduced latency through multi-modal data integration

Abstract

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.