Overload-Robust Latency in 5G-TSN: A HoL-Enhanced Hybrid Lyapunov Approach for 3GPP Indoor Factory Environments

Abstract

Private 5G networks are a key enabler for flexible industrial automation, especially when used in conjunction with Time-Sensitive Networking (TSN) technology. In this context, radio schedulers must multiplex safety-critical control traffic with bandwidth-hungry sensing streams over a fixed spectrum allocation. This paper proposes a Head-of-Line (HoL) Enhanced Hybrid Lyapunov scheduler for 5G-TSN networks that augments a drift-plus-penalty queue-stability core with an explicit head-of-line delay term and a class-isolation mechanism. The scheduler is evaluated in a 3GPP Indoor Factory scenario with standardized 3GPP fading, spatial consistency, and clutter blockage, using Automated Guided Vehicles (AGVs) generating concurrent URLLC, eMBB, and mMTC flows mapped to dedicated QoS-flow bearers. A fleet-size sweep of 5--30 AGVs on a fixed 20\,MHz carrier reveals a scheduler-independent capacity threshold at approximately 12 vehicles, verified by resource-block saturation. Below the threshold, the proposed scheduler is competitive with the strongest delay-aware baselines and its head-of-line term halves the URLLC deadline-miss ratio relative to the plain Lyapunov formulation. Beyond the threshold, it degrades selectively where the baselines collapse: at $2.5\times$ overload it delivers $1.8\times$ more URLLC traffic than the proportional-fair and delay-budget-aware baselines with a $\approx 4$--$7\times$ shorter 99th-percentile latency, resolving the capacity shortfall in favour of the critical classes instead of spreading it across the traffic mix, at a quantified cost in aggregate cell throughput. The results position Lyapunov-based scheduling as an attractive overload-robustness mechanism for industrial 5G deployments that must remain dependable under unexpected load conditions.