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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.26330 |
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Table of Contents:
- The integration of multimodal sensing and millimeter-wave (mmWave) communications is a key enabler for highly mobile vehicle-to-infrastructure (V2I) networks. However, continuous high-resolution visual sensing incurs prohibitive computational energy, while delayed sensing information causes severe beam misalignment. This paper establishes a physics-aware multimodal integrated sensing and communication (M-ISAC) framework that mathematically bridges network-layer queuing delays with physical-layer spatial uncertainty via the semantic age of information (AoI). Guided by this relationship, we aim to strike an optimal trade-off between the tracking posterior Cramer-Rao bound (PCRB) and system energy budgets, we formulate a stochastic mixed-integer non-linear programming (MINLP) problem. Addressing the coupled challenges of temporal computing congestion and non-convex constant modulus constraints, we propose a reinforcement learning (RL) framework empowered by a Lyapunov-driven heterogeneous mixture-of-experts (LD-H-MoE) architecture. By strictly decoupling temporal scheduling and spatial phase mapping into specialized subnetworks, the LD-H-MoE circumvents gradient conflicts prevalent in monolithic multi-task learning. Simulations demonstrate that the proposed LD-H-MoE achieves a highly-effective event-triggered sensing policy, yielding superior tracking accuracy and radio-frequency (RF) resilience while guaranteeing edge computing queue stability and long-term energy budgets.