mlsanityData to 14 Oct 2026

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

arXiv 2509.06775 ↗First posted 8 Sep 2025eess.SY cs.AI cs.ITcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 25 references that are in the map. First posted on arXiv 8 Sep 2025.

Builds on

5 of 25 references.

References · 5

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2025AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challengescited by 169
  3. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  4. 2025Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approachcited by 19
  5. 2022A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challengescited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistencecited by 0

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