Towards Fine-Grained CQF Scheduling for TSN with Temporal Conflict Resolution

Time-Sensitive Networking (TSN) with Cyclic Queuing and Forwarding (CQF) enables deterministic communication in distributed cyber-physical systems. However, existing CQF schedulers model the timeslot as the minimum unit and ignore intra-timeslot dynamics; then, packets arriving within the same timeslot may contend for serialization, leading to temporal conflicts that cause unexpected packet loss and an overestimation of schedulability. To address this issue, we propose a fine-grained CQF scheduling framework that integrates both a refined model and an efficient algorithm. The model replaces timeslot-level injection with precise instants and introduces an intra-timeslot conflict-free constraint to explicitly capture and resolve temporal queue resource conflicts. Building on this model, we further develop a lightweight Fine-Grained Temporal Resource (FGTR) scheduling algorithm, which intelligently allocates injection instants and exploits residual temporal vacancies to improve CQF scheduling performance. Comparative experiments show that FGTR increases the average schedulable scale by 46.1 % and 32.9 % over existing constraint-solving and Deep Reinforcement Learning-based algorithms, respectively, while maintaining high computational efficiency, thereby demonstrating its effectiveness and practicality.