Pablo Ameigeiras

Julia Caleya-Sanchez, Pablo Muñoz, Jorge Sánchez-Garrido et al.

Time synchronization is essential for industrial IoT and Industry 4.0/5.0 applications, but achieving high synchronization accuracy in Time-Sensitive Networking (TSN)-5G networks is challenging due to jitter and asymmetric delays. 3GPP TS 23.501 defines three 5G synchronization modes: time-aware system, boundary clock (BC), and transparent clock (TC), where TC offers a promising solution. However, to the best of our knowledge, there is no empirical evaluation of TC in a TSN-5G network. This paper empirically evaluates an 5G end-to-end TC in a TSN-5G network, implemented on commercial TSN switches with a single clock. For TC development, we compute the residence time in 5G and recover the clock domain at the slave node. We deploy a TSN-5G testbed with commercial equipment for synchronization evaluation by modifying the Precision Timing Protocol (PTP) message transmission rates. Experimental results show a peak-to-peak synchronization of 500 ns, meeting the industrial requirement of < 1 us, with minimal synchronization offsets for specific PTP message transmission rates.

Oscar Adamuz-Hinojosa, Abdelhilah Abdeselam, Pablo Muñoz et al.

This paper studies Radio Access Network (RAN) slicing strategies for 5G Industry~4.0 networks with ultra-reliable low-latency communication (uRLLC) requirements. We comparatively analyze four RAN slicing deployment options that differ in slice sharing and per-line or per-flow isolation. Unlike prior works that focus on management architectures or resource allocation under a fixed slicing structure, this work addresses the design of RAN slicing deployment options in the presence of multiple production lines and heterogeneous industrial flows. An SNC-based analytical framework and a heuristic slice planner are used to evaluate these options in terms of per-flow delay guarantees and radio resource utilization. Results show that under resource scarcity only per-flow slicing prevents delay violations by tightly matching resources to per-flow delay targets, while slice-sharing and hybrid deployments improve aggregation efficiency at the cost of weaker protection for the most delay-critical flows. Execution-time results confirm that the planner operates at Non-RT time scales, enabling its integration within O-RAN Non-RT RIC loops.

Pablo Rodriguez-Martin, Oscar Adamuz-Hinojosa, Pablo Muñoz et al.

Deterministic communications are essential to meet the stringent delay and jitter requirements of Industrial Internet of Things (IIoT) services. IIoT increasingly demands wide-area wireless mobility to support Autonomous Mobile Robots (AMR) and dynamic workflows. Integrating Time-Sensitive Networking (TSN) with 5G private networks is emerging as a promising approach to fulfill these requirements. In this architecture, 5G provides wireless access for industrial devices, which connect to a TSN backbone that interfaces with the enterprise edge/cloud, where IIoT control and computing systems reside. TSN achieves bounded latency and low jitter using IEEE 802.1Qbv Time-Aware Shaper (TAS), which schedules the network traffic in precise time slots. However, the stochastic delay and jitter inherent in 5G disrupt TSN scheduling, requiring careful tuning of TAS parameters to maintain end-to-end determinism. This paper presents an empirical study evaluating the impact of 5G downlink delay and jitter on TAS scheduling using a testbed with TSN switches and a commercial 5G network. Results show that guaranteeing bounded latency and jitter requires careful setting of TAS transmission window offset between TSN switches based on the measured 5G delay bounded by a high order p-th percentile. Otherwise, excessive offset may cause additional delay or even a complete loss of determinism.

Iñaki Ucar, Jorge Navarro-Ortiz, Pablo Ameigeiras et al.

This paper presents an extensible and reusable framework which addresses the problem of video quality assessment over IP networks. The proposed tool (referred to as Video-Tester) supports raw uncompressed video encoding and decoding. It also includes different video over IP transmission methods (i.e.: RTP over UDP unicast and multicast, as well as RTP over TCP). In addition, it is furnished with a rich set of offline analysis capabilities. Video-Tester analysis includes QoS and bitstream parameters estimation (i.e.: bandwidth, packet inter-arrival time, jitter and loss rate, as well as GOP size and I-frame loss rate). Our design facilitates the integration of virtually any existing video quality metric thanks to the adopted Python-based modular approach. Video-Tester currently provides PSNR, SSIM, ITU-T G.1070 video quality metric, DIV and PSNR-based MOS estimations. In order to promote its use and extension, Video-Tester is open and publicly available.