Francisco Javier Rivas

The emergence of cloud-native architectures and Open RAN (O-RAN) principles has revolutionized the deployment and scalability of mobile network infrastructure. However, the energy efficiency of Virtualized Network Functions (VNFs) operating in such environments remains a critical concern, particularly as 5G and 6G networks scale in complexity and resource demands. This study introduces a modular and reproducible testbed for high-fidelity energy profiling of containerized monolithic srsRAN-based gNB implementations running on Commercial Off-The-Shelf (COTS) server-class hardware. The testbed integrates a commercial-grade Power Analyzer (PA) and a full-stack network emulation framework to measure the impact of key parameters, including CPU frequency and core allocation, on the power consumption of a virtualized gNB. A comprehensive configuration dataset is collected across both high-load and low-load scenarios. The results reveal that CPU frequency throttling consistently reduces energy consumption beyond specific performance thresholds, while core limitation is effective only in low-load scenarios; however, it enables VNF co-location, which contributes to reducing overall infrastructure-level energy consumption. These findings validate the applicability of dynamic energy optimization strategies and provide actionable insights for orchestration frameworks aiming to balance energy efficiency with Quality of Service (QoS) requirements. The emergence of cloud-native architectures and Open RAN (O-RAN) principles has revolutionized the deployment and scalability of mobile network infrastructure. However, the energy efficiency of Virtualized Network Functions (VNFs) operating in such environments remains a critical concern, particularly as 5G and 6G networks scale in complexity and resource demands. This study introduces a modular and reproducible testbed for high-fidelity energy profiling of containerized monolithic srsRAN-based gNB implementations running on Commercial Off-The-Shelf (COTS) server-class hardware. The testbed integrates a commercial-grade Power Analyzer (PA) and a full-stack network emulation framework to measure the impact of key parameters, including CPU frequency and core allocation, on the power consumption of a virtualized gNB. A comprehensive configuration dataset is collected across both high-load and low-load scenarios. The results reveal that CPU frequency throttling consistently reduces energy consumption beyond specific performance thresholds, while core limitation is effective only in low-load scenarios; however, it enables VNF co-location, which contributes to reducing overall infrastructure-level energy consumption. These findings validate the applicability of dynamic energy optimization strategies and provide actionable insights for orchestration frameworks aiming to balance energy efficiency with Quality of Service (QoS) requirements.

The deployment of Virtualized Network Functions (VNFs) in Open Radio Access Network (O-RAN) architectures for beyond 5G (b5G) and 6G networks presents a fundamental challenge in resource dimensioning. Network operators must accurately provision computational, memory and network resources to ensure service reliability while maximizing infrastructure efficiency at resource-constrained edge sites. Underprovisioning results in service degradation and outages, while over-provisioning reduces deployment density and increases operational costs. Despite its critical importance, the relationship between the consumption of VNF resources and operational parameters remains insufficiently characterized, dealing with uncertainties in resource planning. In this paper, we introduce SANGRE (Systematic chAracterization of NextG O-RAN VNF rEsource utilization), a framework for comprehensive evaluation of VNF resource requirements under diverse operational conditions. SANGRE employs a wraparound testing methodology to ensure measurement accuracy and repeatability in isolated environments. We implement this framework on a production-grade testbed using open-source gNodeB solutions (SRS) and perform extensive evaluations across multiple network configurations. Our analysis reveals the complex dependencies between the utilization of VNF resources and key parameters such as cell bandwidth, MIMO configuration, user density, and traffic patterns. These findings provide essential information for operators to optimize resource allocation strategies in NextG deployments.

NB-IoT is the first 3GPP technology that has been specifically developed for IoT. In this paper, we will introduce the most important NB-IoT features included in Release 14, Release 15, and Release 16 and the need for testing of NB-IoT solutions. This paper is focused on demonstrating the potential of the new experimentation features included into the TRIANGLE testbed to support testing and benchmarking of Narrowband Internet of Thing (NB-IoT) solutions. TRIANGLE provides an experimentation framework that offers a high level of abstraction using a framework that allows for defining and executing experiments in a very straightforward way. The experimentation framework developed as part of the TRIANGLE project optionally keeps experimenters far from the low level configuration of the infrastructure and provides scenarios which reproduce realistic conditions.

The characterization of traffic performance in cellular networks from the user viewpoint is a costly process involving a large range of protocols and parameters with complex dependencies. Dealing with this challenge requires cross-layer measurements and correlated analysis. The characterization methodology proposed here supports the collection of key information for troubleshooting IP communications problems and correlating them with propagation issues, such as cell changes or link outages, and resource allocation problems at specific geographical locations. The methodology is based on a monitoring and recording tool for smartphones and a correlation tool chain. With the results obtained from the methodology's deployment, mobile operators, services providers, and mobile developers could gain access to real user experiences and specific users' data to improve radio coverage, adapt services, and customize mobile applications and protocols based on IP to cope with mobility issues in cellular networks.

We introduce a real-time experimentation testbed in this paper which enables more realistic analysis of quality of service (QoS) in LTE networks. This testbed is envisioned for the improvement of QoS and quality of experience (QoE) through the experimentation with real devices, services, and radio configurations. Radio configurations suggested in the literature typically arise from simulations; the testbed provides a real and controlled testing environment where such configurations can be validated. The added value of this testbed goes a long way not only in the provision of more realistic results but also in the provision of QoS and QoE cross-layer measurements through the correlation of information collected at different layers: from service and IP levels to radio and protocol parameters. Analyzing the interlayer dependencies will allow us to identify optimal settings for the radio access network and service parameters. This information can be used to suggest new cross-layer optimizations to further improve quality of experience of mobile subscribers. As a use case, we examine VoIP service over LTE, which is currently an open issue.

Railway transportation uses specific technologies such as GSM-R for traffic signaling purposes, but commercial networks could also provide added value services to passengers and transport companies such as CCTV or telemetry. Furthermore mobile users increasingly want to be always connected even when they are traveling. Although current deployed technologies such as 3G provide wide coverage in general, when it comes to high mobility scenarios such as the railway, further analysis is required to ensure appropriate user experience. In this paper we propose a methodology to collect and to organize traffic information related to the behavior of Internet services over commercial mobile networks (UMTS and HSDPA). We apply our method to conduct an extensive study on a high speed train line in the south of Spain. The measurement campaigns comprised hundreds of VoIP sessions, comparing different network providers over more than 155 kilometers. We conclude that the characterization method is useful to provide information for railway operators (for instance, to migrate from GSM-R to LTE), telecom operators (to fit their network deployments) and for mobile software developers (to adapt their applications to the high speed environment).

This application track paper describes an experimental test setup composed to verify the communication performance of internet applications over LTE (Long Term Evolution) networks.