Policy-Driven DRL-Based TXOP Adaptation in NR-U and Wi-Fi Coexistence

Abstract

The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a challenging coexistence management problem, where heterogeneous channel access mechanisms lead to a significant imbalance in spectrum utilization and degraded Wi-Fi performance. To address this challenge, we propose a policy-driven deep reinforcement learning (DRL) framework for adaptive transmission opportunity (TXOP) control, in which the coexistence process is formulated as a Markov decision process (MDP) and a deep Q-network (DQN) learns control policies through online interaction. A key contribution is the introduction of a policy layer via reward design, enabling explicit control of coexistence tradeoffs among fairness, throughput, and utility. Three policies, namely absolute fairness, moderate fairness, and utility-based fairness, are developed to achieve different operating points. Simulation results show that the proposed framework achieves a Jain fairness index above 0.9 under strict fairness control. Compared to absolute fairness, moderate fairness improves aggregate throughput by 68.22%, while the utility-based policy further enhances utility by 177.6%. These results demonstrate that policy-driven control provides a flexible and effective solution for managing tradeoffs in heterogeneous coexistence networks.