用智能体DDQN优化5G侧链频谱分配，显著降低阻塞率。
Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks
- 构建多维感知智能体，综合考虑延迟、信道质量等动态因素。
- 在带宽受限下阻塞率降低87.5%，且保持吞吐量稳定。
- 适合边缘计算场景的高效资源调度，适用于车联网等低时延应用。
本文提出一种基于智能体双深度Q网络（DDQN）的调度器，用于5G新无线电（NR）侧链（SL）网络中的授权与非授权频段分配。该智能体不仅感知队列延迟、链路质量、共存强度和切换稳定性等多维上下文，还采用兼顾容量与服务质量（QoS）的奖励机制，引导其做出目标导向的调度决策。相比传统阈值策略，在带宽受限条件下，所提方案将阻塞率降低高达87.5%，同时维持吞吐量水平。该调度器为面向任务的轻量化智能体（E-agent），专为网络边缘的高效资源调度设计。
原文摘要 · Abstract (English)
In this paper, we present an agentic double deep Q-network (DDQN) scheduler for licensed/unlicensed band allocation in New Radio (NR) sidelink (SL) networks. Beyond conventional reward-seeking reinforcement learning (RL), the agent perceives and reasons over a multi-dimensional context that jointly captures queueing delay, link quality, coexistence intensity, and switching stability. A capacity-aware, quality of service (QoS)-constrained reward aligns the agent with goal-oriented scheduling rather than static thresholding. Under constrained bandwidth, the proposed design reduces blocking by up to 87.5% versus threshold policies while preserving throughput, highlighting the value of context-driven decisions in coexistence-limited NR SL networks. The proposed scheduler is an embodied agent (E-agent) tailored for task-specific, resource-efficient operation at the network edge.
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