mlsanityData to 14 Oct 2026

QoS-Aware State-Augmented Learnable Framework for 5G NR-U/Wi-Fi Coexistence: Impact of Parameter Selection and Enhanced Collision Resolution

arXiv 2512.13393 ↗First posted 15 Dec 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Unlicensed spectrum supports diverse traffic with stringent Quality-of-Service (QoS) requirements. In NR-U/Wi-Fi coexistence,the values of MAC parameters critically influence delay, collision behavior, and airtime fairness and efficiency. In this paper, we investigate the impact of (i) cost scaling and violation modeling, (ii) choice of MAC parameters, and (iii) an enhanced collision resolution scheme for the Listen-Before-Talk (LBT) mechanism on the performance of a state-augmented constrained reinforcement learning controller for NR-U/Wi-Fi coexistence. Coexistence control is formulated as a constrained Markov decision process with an explicit delay constraint for high-priority traffic and fairness as the optimization goal. Our simulation results show three key findings: (1) signed, threshold-invariant cost scaling with temporal smoothing stabilizes learning and strengthens long-term constraint adherence; (2) use of the contention window parameter for control provides smoother adaptation and better delay compliance than other MAC parameters; and (3) enhanced LBT significantly reduces collisions and improves airtime efficiency. These findings provide practical insights for achieving robust, QoS-aware coexistence control.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 14 references that are in the map. First posted on arXiv 15 Dec 2025.

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  1. 2025Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning1 Mar

Other references · 5

  1. 2021State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewardscited by 40
  2. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 14
  3. 20205G New Radio Unlicensed: Challenges and Evaluationcited by 3
  4. 2025QaSAL: QoS-aware State-Augmented Learnable Algorithms for Coexistence of 5G NR-U/Wi-Ficited by 1
  5. 2025QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Managementcited by 1

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  7. 2024Federated Reinforcement Learning for Uplink Centric Broadband Communication Optimization over Unlicensed Spectrum ↗
  8. 2026Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization