{"ID":2873488,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06775","arxiv_id":"2509.06775","title":"Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks","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.","short_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 de...","url_abs":"https://arxiv.org/abs/2509.06775","url_pdf":"https://arxiv.org/pdf/2509.06775v3","authors":"[\"Po-Heng Chou\",\"Pin-Qi Fu\",\"Walid Saad\",\"Li-Chun Wang\"]","published":"2025-09-08T14:58:12Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"cs.IT\",\"cs.LG\",\"cs.NI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}