Heterogeneous Mixture-of-Experts for Energy-Efficient Multimodal ISAC in Highly Mobile Networks

TL;DR

This paper introduces a physics-aware multimodal ISAC framework for V2I networks, balancing sensing energy and communication reliability, and proposes a novel RL-H-MoE architecture to optimize sensing policies.

Contribution

It presents a new reinforcement learning architecture that decouples temporal and spatial challenges, improving energy efficiency and reliability in highly mobile multimodal networks.

Findings

Achieves an optimal event-triggered sensing policy.

Significantly reduces long-term system cost.

Guarantees ultra-low sensing errors and reliable links.

Abstract

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 worsens beam misalignment. In this paper, we establish a physics-aware multimodel integrated sensing and communication (M-ISAC) framework that quantifies the mathematical trade-off between sensing energy and communication reliability using the semantic age of information (AoI). To address the coupled challenges of temporal AoI evolution and instantaneous non-convex constant modulus constraints, we propose a novel reinforcement learning approach empowered by a heterogeneous mixture-of-experts (RL-H-MoE) architecture. By strictly decoupling the temporal scheduling and spatial phase mapping, the RL-H-MoE avoids prevalent gradient conflicts in multi-task learning. Extensive simulations demonstrate that the proposed architecture achieves an optimal event-triggered sensing policy, significantly minimizing the long-term system cost while guaranteeing ultra-low sensing errors and reliable physical-layer link connectivity.