面向RAN切片的智能量子深度强化学习
Agentic Quantum Deep Reinforcement Learning for RAN Slicing
- University of Ottawa（渥太华大学）
- Ericsson, Canada（爱立信（加拿大））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对RAN切片的可靠性-吞吐量权衡问题，提出Agentic-QDRL两时间尺度框架，结合PMAR控制器与VQC调度器，经仿真验证可提升eMBB性能并维持URLLC可靠性。
AI中文摘要：
无线接入网（RAN）切片使超可靠低延迟通信（URLLC）和增强移动宽带（eMBB）服务能够共享无线资源，但它们的需求构成了极具挑战性的可靠性-吞吐量权衡。URLLC需要低延迟和可靠的数据包传输，而eMBB则以高持续吞吐量为目标。本文考虑下行链路URLLC/eMBB RAN切片问题，将其表述为队列感知的长期eMBB吞吐量最大化问题，约束条件包括URLLC延迟违规、物理资源块（PRB）排他性和切片预算约束。为解决该问题，我们提出Agentic量子深度强化学习（Agentic-QDRL），这是一种两时间尺度框架，结合了智能切片级资源控制与量子增强PRB调度。在慢时间尺度上，感知-记忆-行动-反思（PMAR）控制器调整URLLC和eMBB切片的资源份额；在快时间尺度上，基于紧凑变分量子电路（VQC）的QDRL调度器在当前切片配置下执行PRB分配。我们还引入了可行性投影和安全 fallback 机制，以满足调度约束并减少URLLC截止日期违规。在不同eMBB流量负载下的仿真结果显示，与经典DRL和启发式基线相比，Agentic-QDRL可提升eMBB吞吐量、减少eMBB队列堆积，并维持URLLC延迟可靠性。
英文摘要：
Radio access network (RAN) slicing enables ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services to share radio resources, but their requirements create a challenging reliability--throughput tradeoff. URLLC requires low-latency and reliable packet delivery, whereas eMBB targets high sustained throughput. This paper considers downlink URLLC/eMBB RAN slicing and formulates it as a queue-aware long-term eMBB throughput maximization problem subject to URLLC delay-violation, physical resource block (PRB) exclusivity, and slice-budget constraints. To solve this problem, we propose agentic quantum deep reinforcement learning (Agentic-QDRL), a two-time-scale framework that combines agentic slice-level resource control with quantum-enhanced PRB scheduling. At the slow time scale, a perceive--memory--act--reflect (PMAR) controller adapts the resource shares of URLLC and eMBB slices. At the fast time scale, a compact variational quantum circuit (VQC)-based QDRL scheduler performs PRB allocation under the current slice configuration. A feasibility projection and a safety fallback mechanism is further introduced to satisfy scheduling constraints and reduce URLLC deadline violations. Simulation results under different eMBB traffic loads show that Agentic-QDRL improves eMBB throughput, reduces eMBB queue buildup, and maintains URLLC delay reliability compared with classical DRL and heuristic baselines.
