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This research proposes a fairness-guided Deep Q-Network (DQN) to optimize transmission opportunities for NR-U and Wi-Fi coexistence, directly addressing the imbalance caused by differing contention parameters. By comparing strict throughput balance against broader fairness regions, the study quantifies the trade-offs between equitable resource distribution and spectral efficiency in unlicensed spectrum environments. While the core application involves networking protocols, the central contribution is the development and evaluation of a reinforcement learning agent specifically designed to solve AI-driven spectrum sharing challenges.
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Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence

arXiv2 min read

arXiv:2605.00457v5 Announce Type: replace-cross Abstract: New Radio operating in unlicensed spectrum (NR-U) and Wi-Fi employ different contention parameters and channel-occupancy durations, which can produce severe coexistence imbalance. This correspondence develops a fairness-guided deep Q-network (DQN) that adjusts the NR-U transmission opportunity (TXOP) using the observed per-node throughput ratio. Two reward designs, Q1 and Q2, expose complementary operating regimes. Q1 promotes strict per-node throughput balance, whereas Q2 admits a broader inter-radio-access-technology fairness region and rewards throughput improvement in an intermediate fairness band. The policies are compared with the default Third Generation Partnership Project (3GPP) listen-before-talk (LBT), an adapted multi-armed-bandit method, a utility-fairness-based tabular Q-learning comparator, and a default-referenced fixed-TXOP baseline. Experiments across four priority classes and asymmetric populations report aggregate throughput, user-level Jain fairness, and concave throughput utility over 20 independent trials. The results characterize the cost of strict fairness, efficiency recovery under moderate relaxation, and transfer from symmetric to asymmetric deployments.

Abstract:New Radio operating in unlicensed spectrum (NR-U) and Wi-Fi employ different contention parameters and channel-occupancy durations, which can produce severe coexistence imbalance. This correspondence develops a fairness-guided deep Q-network (DQN) that adjusts the NR-U transmission opportunity (TXOP) using the observed per-node throughput ratio. Two reward designs, Q1 and Q2, expose complementary operating regimes. Q1 promotes strict per-node throughput balance, whereas Q2 admits a broader inter-radio-access-technology fairness region and rewards throughput improvement in an intermediate fairness band. The policies are compared with the default Third Generation Partnership Project (3GPP) listen-before-talk (LBT), an adapted multi-armed-bandit method, a utility-fairness-based tabular Q-learning comparator, and a default-referenced fixed-TXOP baseline. Experiments across four priority classes and asymmetric populations report aggregate throughput, user-level Jain fairness, and concave throughput utility over 20 independent trials. The results characterize the cost of strict fairness, efficiency recovery under moderate relaxation, and transfer from symmetric to asymmetric deployments.

5 pages, 2 figures, 1 table. Submitted to IEEE Transactions on Vehicular Technology

Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Systems and Control (eess.SY)

C.2.1; I.2.6; C.4

arXiv:2605.00457 [cs.NI]

(or arXiv:2605.00457v5 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2605.00457 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 263 words

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