# QoS-aware State-Augmented Learnable Algorithm for Wireless Coexistence Parameter Management

arXiv: https://arxiv.org/abs/2506.22652 · HTML: https://mlsanity.com/paper/2506.22652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

Efficient and fair coexistence in unlicensed spectrum is essential to support heterogeneous networks such as 5G NR-U and Wi-Fi, which often contend for shared wireless resources. We introduce a general framework for wireless Coexistence Parameter Management (CPM) based on state-augmented constrained reinforcement learning. We propose a novel algorithm, QaSAL-CPM, which incorporates state-augmentation by embedding the dual variables in the constrained optimization formulation directly into the agent's observation space. This method enables the agent to respond to constraint violations in real time while continuing to optimize a primary performance objective. Through extensive simulations of 5G NR-U and Wi-Fi coexistence scenarios, we show that QaSAL-CPM achieves reliable QoS compliance and improved policy robustness across various transmitter densities compared to previous approaches. The proposed framework offers a scalable and adaptive solution for real-time coexistence optimization in next-generation wireless networks.