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

arXiv: https://arxiv.org/abs/2605.00457 · HTML: https://mlsanity.com/paper/2605.00457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 1 May 2026.

## Builds on (most cited first, 7 of 7)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges](https://mlsanity.com/paper/2202.11082/) (2022)
- [Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning](https://mlsanity.com/paper/2503.00256/) (2025)
- [Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks](https://mlsanity.com/paper/2509.06775/) (2025)
- [Unlicensed Band Allocation for Heterogeneous Networks](https://mlsanity.com/paper/2509.23216/) (2025)
- [Modeling the Unlicensed Band Allocation for LAA With Buffering Mechanism](https://mlsanity.com/paper/2509.23217/) (2025)
- [Markov Modeling for Licensed and Unlicensed Band Allocation in Underlay and Overlay D2D](https://mlsanity.com/paper/2509.23218/) (2025)

## Abstract (arXiv)

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.