Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks

Po-Heng Chou, Pin-Qi Fu, Walid Saad, Li-Chun Wang
9/8/2025
eess.SYcs.AIcs.IT

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Abstract

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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Cite this paper

@article{chou2025agentic,
  title  = {Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks},
  author = {Po-Heng Chou and Pin-Qi Fu and Walid Saad and Li-Chun Wang},
  year   = {2025},
  eprint = {2509.06775},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.06775v3}
}

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