Enhancing deterministic transmission in Time-Sensitive Networking: A Joint Guard Band Compression and Non-Disruptive Frame Preemption model

Abstract

In the Industrial Internet of Things (IIoT), the real-time interaction of critical control and sensing data imposes stringent requirements on network determinism and reliability. Time-Sensitive Networking (TSN) is widely utilized to ensure low latency and high-precision synchronization; however, efficiently coordinating the nanosecond-level determinism of Time-Triggered (TT) traffic with the high throughput of Event-Triggered (ET) traffic remains a core challenge. Existing Guard Band mechanisms lead to significant bandwidth waste due to fixed reserved windows, while traditional frame preemption mechanisms, though improving efficiency, still suffer from unpredictable TT frame delays. To address these issues, this paper proposes and validates two novel frame preemption strategies. First, a Guard Band Compression and Non-Disruptive Frame Preemption model (GCNFP) is introduced, which compresses the guard band to 76 bytes and employs a padding mechanism to ensure zero-offset handling of ET frames within the guard window, thus eliminating transmission deviations caused by ET blocking and inter-frame gaps (IFG). Second, an enhanced model with judgment mechanism (JM-GCNFP) is developed, which dynamically monitors ET frame transmission within the guard window and delays preemption until the optimal moment. This adaptive strategy maximizes the use of available time slots while reducing unnecessary preemption and padding overhead. Simulation results show that both strategies achieve zero-offset scheduling and significantly improve system performance. Specifically, JM-GCNFP reduces the maximum ET frame delay by more than 40% and improves bandwidth utilization by nearly 20% under high-load scenarios, demonstrating the advantages in achieving enhanced network determinism and transmission efficiency.