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arXiv Machine Learning By Po-Heng Chou, Yi-Fang Yu, Shou-Yu Chen, Chiapin Wang

Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence

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The paper proposes a fairness‑guided deep Q‑network (DQN) to control the transmission opportunity (TXOP) of NR‑U in unlicensed spectrum, aiming to balance coexistence with Wi‑Fi. Two reward designs, Q1 and Q2, target different fairness regimes: Q1 enforces strict per‑node throughput balance, while Q2 allows a broader fairness band and rewards throughput gains within it. The DQN policies are benchmarked against 3GPP LBT, a multi‑armed‑bandit approach, utility‑fairness tabular Q‑learning, and a fixed‑TXOP baseline, with experiments across four priority classes and asymmetric populations measuring aggregate throughput, Jain fairness, and concave throughput utility.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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