2025/06/27 by Mohammad Fasihi, Fasihi, Mohammad Reza, Brian L. Mark +1 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cognitive Radio Networks and Spectrum Sensing #FOS: Electrical engineering #Systems and Control (eess.SY) #Wireless Networks and Protocols #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2506.22652
openalex publication_date 2025/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.