arXiv subjects

Imanol Martinez

Publications and source records attributed to Imanol Martinez.

2 recordsLinked to original sources

arXiv2026

5G Configured Grant Scheduling for 5G-TSN Integration for the Support of Industry 4.0

Factories are evolving towards digitalized data-based ecosystems under the paradigm of the Industry 4.0 where new industrial services allow the implementation of more robust, resilient and customized manufacturing systems. Such services (e.g., digital twins, extended reality or cooperative robots) will require highly reliable and deterministic communication networks capable of supporting stringent latency and reliability requirements. 5G networks and their future evolution have the necessary capabilities to meet these requirements. However, the use of 5G in industrial environments requires its effective and efficient integration with Time Sensitive Networking (TSN), which is becoming the standard wired technology for Industry 4.0 environments. TSN provides unprecedented deterministic service levels with perfectly bounded latencies. The integration of the industrial 5G and TSN networks will be key to support the flexibility and determinism demanded by the Industry 4.0 paradigm. A critical aspect to achieve this integration is the coordination of the schedulers of both networks. TSN has information about the capabilities of the 5G-TSN integrated network, and it is in charge of deciding the path and scheduling for each TSN traffic flow. The scheduling in 5G must be done according to the scheduling decisions and information provided by TSN to guarantee the end-to-end latency requirements of TSN traffic. In this context, this paper proposes a novel Configured Grant scheduling scheme for 5G integrated into a TSN network that aims to meet the latency requirements of the different TSN flows. The proposed scheme exploits the information provided by TSN about the characteristics of the TSN traffic to coordinate its decision with the scheduling of TSN. This study demonstrates that the proposed scheduling scheme considerably increases the number of TSN flows that can be satisfactorily served.

cs.NI↗

arXiv2026

An open-source implementation and validation of 5G NR Configured Grant for URLLC in ns-3 5G LENA: a scheduling case study in Industry 4.0 scenarios

Factories are undergoing a digital transformation towards cost-efficient, zero-defect manufacturing, creating the need for communication networks capable of meeting stringent latency and reliability requirements. 5G and beyond networks are being designed to support Ultra-Reliable Low-Latency Communications (URLLC). However, the use of dynamic scheduling for uplink transmissions introduces additional latency due to the signaling required to request and allocate radio resources. To reduce this latency, 5G NR defines Configured Grant (CG), which pre-allocates uplink resources and eliminates the need for per-packet scheduling requests. Accurate simulation tools implementing URLLC features are essential to evaluate the capability of 5G networks to support time-critical services and to investigate new solutions. Nevertheless, the availability of such tools is limited and, to the best of the authors' knowledge, no open-source 5G NR simulator currently supports CG. To address this gap, this paper presents the first implementation of CG in an open-source 5G NR simulator. Specifically, CG has been integrated into the ns-3 5G-LENA system-level simulator and made publicly available. In addition, the Orthogonal Frequency Division Multiple Access (OFDMA) implementation has been enhanced to better reflect the flexibility of 5G NR. The implementation is validated through Industry 4.0 scenarios, where the latency performance achieved with CG under different scheduling policies is analyzed. Results show that the latency obtained with 5G-LENA closely matches that reported in previous analytical studies. Furthermore, the study highlights the importance of efficient radio resource utilization to reduce latency and satisfy the requirements of critical industrial services.

cs.NI↗