Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics

• URL: http://arxiv.org/abs/2408.13546v1
• Date: Sat, 24 Aug 2024 10:35:10 GMT
• Title: Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics
• Authors: Zonghui Yang, Shijian Gao, Xiang Cheng, Liuqing Yang,
• Abstract summary: Integrated sensing and communication (ISAC) technology plays a crucial role in vehicular networks. Double dynamics present significant challenges for real-time ISAC precoding design. We propose a synesthesia of machine (SoM)-enhanced precoding paradigm.
• Score: 15.847713094328286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrated sensing and communication (ISAC) technology plays a crucial role in vehicular networks. However, the communication channel within this context exhibits time-varying characteristics, and potential targets may move rapidly, resulting in double dynamics. These presents significant challenges for real-time ISAC precoding design that have not been thoroughly explored. While optimization-based precoding methods have been extensively studied, they are computationally complex and heavily rely on perfect prior information that is rarely available in situations with double dynamics. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm, where the base station leverages various modalities such as positioning and channel information to adapt to double dynamics, and effectively utilizes environmental information to stretch ISAC performance boundaries through a deep reinforcement learning framework. Additionally, a parameter-shared actor-critic architecture is tailored to expedite training in complex state and action spaces. Extensive experimental validation has demonstrated the multifaceted superiority of our method over existing approaches.

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