Synesthesia of Machines (SoM)-Enhanced ISAC Precoding for Vehicular Networks with Double Dynamics
This addresses the problem of efficient integrated sensing and communication for vehicular networks, but it appears incremental as it builds on existing deep reinforcement learning techniques.
The paper tackles the challenge of real-time precoding design in vehicular networks with time-varying channels and rapid target movement by proposing a synesthesia of machine-enhanced precoding paradigm using deep reinforcement learning, achieving superior performance over existing methods in experiments.
Integrated sensing and communication (ISAC) technology is vital for vehicular networks, yet the time-varying communication channels and rapid movement of targets present significant challenges for real-time precoding design. Traditional optimization-based methods are computationally complex and depend on perfect prior information, which is often unavailable in double-dynamic scenarios. In this paper, we propose a synesthesia of machine (SoM)-enhanced precoding paradigm that leverages modalities such as positioning and channel information to adapt to these dynamics. Utilizing a deep reinforcement learning (DRL) framework, our approach pushes ISAC performance boundaries. We also introduce a parameter-shared actor-critic architecture to accelerate training in complex state and action spaces. Extensive experiments validate the superiority of our method over existing approaches.