Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915518936514560 |
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| author | Chou, Po-Heng Fu, Pin-Qi Saad, Walid Wang, Li-Chun |
| author_facet | Chou, Po-Heng Fu, Pin-Qi Saad, Walid Wang, Li-Chun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06775 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks Chou, Po-Heng Fu, Pin-Qi Saad, Walid Wang, Li-Chun Systems and Control Artificial Intelligence Information Theory Machine Learning Networking and Internet Architecture 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. |
| title | Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks |
| topic | Systems and Control Artificial Intelligence Information Theory Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2509.06775 |