Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience
원문 초록
Adaptive beam switching in 6G networks is challenged by high frequencies, mobility, and blockage. We propose an Online Learning framework using Deep Reinforcement Learning (DRL) with an enhanced state representation (velocity and blockage history), a GRU architecture, and prioritized experience replay for real-time beam optimization. Validated via Nvidia Sionna under time-correlated blockage, our approach significantly enhances resilience in SNR, throughput, and accuracy compared to a conventional heuristic. Furthermore, the enhanced DRL agent outperforms a reactive Multi-Armed Bandit (MAB) baseline by leveraging temporal dependencies, achieving lower performance variability. This demonstrates the benefits of memory and prioritized learning for robust 6G beam management, while confirming MAB as a strong baseline.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning
GymPN: A Library for Decision-Making in Process Management Systems
Learning-Based Resource Management in Integrated Sensing and Communication Systems
Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Algorithmic Control Improves Residential Building Energy and EV Management when PV Capacity is High but Battery Capacity is Low
토론
이 논문과 연결된 포럼 글이 아직 없습니다.