A new LD-H-MoE RL framework improves tracking accuracy and energy efficiency in energy-constrained multimodal ISAC by decoupling scheduling and beam phase tasks while ensuring queue stability.
Meta-reinforcement learning optimization for movable antenna- aided full-duplex cf-dfrc systems with carrier frequency offset,
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Optimizing Tracking Accuracy in Energy-Constrained Multimodal ISAC via Lyapunov-Driven Heterogeneous Mixture-of-Experts
A new LD-H-MoE RL framework improves tracking accuracy and energy efficiency in energy-constrained multimodal ISAC by decoupling scheduling and beam phase tasks while ensuring queue stability.