Achieving High Average User-Perceived Throughput (UPT): Multi-User Scheduling for Downlink Transmissions

This paper addresses the challenge of achieving high average User-Perceived Throughput (UPT) in multi-user downlink transmissions, which is essential for immersive experience in emerging 6G applications like extended reality and digital twins. Achieving high UPT involves effectively managing burst data traffic over fluctuating wireless channels, a topic not extensively explored in current literature. We approach this by modeling the system as a Markov Decision Process (MDP), focusing on maximizing average UPT by minimizing the average time users have non-empty queues while maintaining system stability. We introduce a multi-user scheduling scheme for a system with a single Resource Block (RB). Specifically, the scheduling metric is a fluid approximation of the minimum average time with non-empty queues, which is obtained by analytically solving a convex optimization problem. We next extend our scheduling scheme to handle multiple spectrum RBs through successive allocation of available RBs, achieving polynomial complexity. Using a quadratic Lyapunov drift method, we ensure scheduler stability. The scheduler is also modified to operate online, adapting to time-varying channel conditions. Simulation results demonstrate that our scheduler not only achieves near-optimal performance but also significantly outperforms existing benchmarks.