# Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.00256 · HTML: https://mlsanity.com/paper/2503.00256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities & Challenges](https://mlsanity.com/paper/2006.16534/) (2020)
- [5G New Radio Unlicensed: Challenges and Evaluation](https://mlsanity.com/paper/2012.10937/) (2020)
- [Heterogenous Networks: From small cells to 5G NR-U](https://mlsanity.com/paper/2112.14240/) (2021)

## Built on by (most cited first, 3 of 3)

- [QoS-Aware State-Augmented Learnable Framework for 5G NR-U/Wi-Fi Coexistence: Impact of Parameter Selection and Enhanced Collision Resolution](https://mlsanity.com/paper/2512.13393/) (2025)
- [Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence](https://mlsanity.com/paper/2605.00457/) (2026)
- [Channel-Token Attention for Reliable Dynamic Spectrum Access under Bursty Primary-User Traffic](https://mlsanity.com/paper/2608.22992/) (2026)

## Abstract (arXiv)

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks.