QoS-Aware State-Augmented Learnable Framework for 5G NR-U/Wi-Fi Coexistence: Impact of Parameter Selection and Enhanced Collision Resolution

TL;DR

This paper presents a QoS-aware reinforcement learning framework for NR-U/Wi-Fi coexistence, analyzing parameter impacts and proposing enhancements to collision resolution to improve delay, fairness, and efficiency.

Contribution

It introduces a state-augmented constrained reinforcement learning controller with an improved collision resolution scheme, providing practical insights for robust coexistence management.

Findings

Cost scaling stabilizes learning and enforces constraints.

Contention window control yields smoother delay compliance.

Enhanced LBT reduces collisions and boosts airtime efficiency.

Abstract

Unlicensed spectrum supports diverse traffic with stringent Quality-of-Service (QoS) requirements. In NR-U/Wi-Fi coexistence,the values of MAC parameters critically influence delay, collision behavior, and airtime fairness and efficiency. In this paper, we investigate the impact of (i) cost scaling and violation modeling, (ii) choice of MAC parameters, and (iii) an enhanced collision resolution scheme for the Listen-Before-Talk (LBT) mechanism on the performance of a state-augmented constrained reinforcement learning controller for NR-U/Wi-Fi coexistence. Coexistence control is formulated as a constrained Markov decision process with an explicit delay constraint for high-priority traffic and fairness as the optimization goal. Our simulation results show three key findings: (1) signed, threshold-invariant cost scaling with temporal smoothing stabilizes learning and strengthens long-term constraint adherence; (2) use of the contention window parameter for control provides smoother adaptation and better delay compliance than other MAC parameters; and (3) enhanced LBT significantly reduces collisions and improves airtime efficiency. These findings provide practical insights for achieving robust, QoS-aware coexistence control.