The rapid evolution of cellular networks has made them a critical infrastructure for modern society, but it has also led to quickly increasing energy consumption. Open Radio Access Network (O-RAN), with its disaggregated architecture, offers significant potential for improving energy efficiency (EE) in next-generation networks. This survey presents a comprehensive review of state-of-the-art O-RAN energy efficiency research, structured around three key dimensions: energy measurement, energy modeling, and energy optimization. We examine both software- and hardware-based measurement techniques, highlighting their strengths, limitations, and applicability on different network functions. The survey then categorizes existing energy consumption models into theoretical and empirical approaches, with a discussion of their methodologies and constraints. Finally, we explore energy optimization strategies defined by O-RAN specifications and beyond, revealing diverse technical directions in academia and industry. The survey also highlights emerging research challenges, including the lack of high-fidelity and generic datasets, the need for standardized evaluation frameworks and methodologies, and the impact of continuous O-RAN evolution on the validity of existing studies. Overall, this work aims to serve as a foundational reference for researchers and engineers to build sustainable and energy-aware O-RAN networks.

Ensuring user privacy remains critical in mobile networks, particularly with the rise of connected devices and denser 5G infrastructure. Privacy concerns have persisted across 2G, 3G, and 4G/LTE networks. Recognizing these concerns, the 3rd Generation Partnership Project (3GPP) has made privacy enhancements in 5G Release 15. However, the extent of operator adoption remains unclear, especially as most networks operate in 5G Non Stand Alone (NSA) mode, relying on 4G Core Networks. This study provides the first qualitative and experimental comparison between 5G NSA and Stand Alone in real operator networks, focusing on privacy enhancements addressing top 8 pre-5G attacks based on recent academic literature. Also, it evaluates the privacy levels of OpenAirInterface, a leading open-source software for 5G, against real network deployments for the same attacks, revealing two new 5G privacy vulnerabilities, and underscoring the need for further studies and stricter standards.

The challenge of evaluating and modeling Quality of Experience (QoE) for content streaming video services is discussed in this paper. Based on the QoE evaluation methodology proposed by the EU-funded TRIANGLE project, a novel no-reference QoE model is presented to predict the user Mean Opinion Score (MOS) for videos streamed services such as YouTube. The study has been performed in a testbed that can control radio and network parameters, enabling real-life scenarios for mobile applications and devices in a controllable and repeatable manner. This work takes advantage of Amazon Mturk as a crowdsourcing platform to collect user opinions at scale, efficiently and quickly harvesting opinion scores from a much more diverse population than it would be possible from a local human panel. The performance of the proposed no-reference model is compared with PEVQ-S, a full-reference standardized algorithm used in the industry, showing better performance but with a much simpler approach. The performance has also been evaluated in SQoE-III, a publicly available adaptive video streaming database, to ensure the robustness of the model.

This paper addresses the challenges associated with modeling and evaluation of Quality of Experience (QoE) for content streaming video services. A novel full-reference QoE model is introduced, aiming to predict the user Mean Opinion Score (MOS) for videos streamed from any content streaming platform, although focusing on YouTube. Screen captures of streamed videos were used for an immersive user-experience investigation, avoiding reliance solely on packet analysis. FFmpeg, a widely-used open-source video processing software, and VMAF, an open-source video quality metric by Netflix, are the primary tools used by the proposed approach. The study is carried out in a testbed that allows full control of the radio and network parameters, enabling the emulation of realistic scenarios of mobile networks and establishing actual 5G connections with commercial mobile devices. To gather a diverse range of user opinions and conveniently scale up the dataset, Amazon MTurk is used as a crowdsourcing platform. Data from real users scoring 150 distorted video sequences have been used to train the machine learning algorithm based on QoE model developed in this work. The performance of the proposed model is compared with PEVQ-S, which is a standardized full reference algorithm showing better performance in a comprehensive set of performance metrics.

This paper presents the TRIANGLE testbed approach to score the Quality of Experience (QoE) of mobile applications, based on measurements extracted from tests performed on an end‐to‐end network testbed. The TRIANGLE project approach is a methodology flexible enough to generalize the computation of the QoE for any mobile application. The process produces a final TRIANGLE mark, a quality score, which could eventually be used to certify applications.

This paper describes PerformNetworks, which is an enhanced test and research facility for cutting edge mobile communications managed by the MORSE group in the University of Malaga. This facility is an evolution, containing key improvements introduced on top of the work presented in NOMS 2012 [1]. The test environment has been extended to support new features available in LTE-Advanced networks, power consumption measurements and channel emulation in uplink. The PerformNetworks testbed is a reference research platform that has been used in multiple experiments.

Voice services has been for a very long time the primary source of income for mobile network operators. Even with the increasing importance of the data traffic, voice services will keep playing a key role and will not disappear with the transition to a networking model based on the IP protocol. Moreover, the key industry players diagnosed years ago that mobile subscribers wouldn't accept a significant degradation in the quality of their voice services. Because of that, it becomes critical to ensure the Quality of Experience (QoE) in the transition to new generation networks based on packet switching communications. The work in the present thesis has aimed to analyze the behavior and the dependencies in the different voice services over IP (VoIP), as well as to identify optimal configurations, potential enhancements and methodologies to ensure acceptable quality levels while minimizing the costs.

In this paper we address the support of wearable mHealth applications in LTE and future 5G networks following a holistic approach that spans across the elements of a mobile network. The communication requirements change from one application to another so we propose a measurement methodology to facilitate the selection of the user equipment to fulfil these requirements. We also discuss a new network architecture to support traffic prioritization, RAN programmability, low latency and group communications to over-the-top applications. Our proposal is validated using several realistic experimentation platforms and the results show that mHealth systems can benefit from our approach.

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 demonstration shows a methodology to collect and analyze key performance indicators (KPI) directly from real devices running commercial mobile applications. The main objective of this methodology is to provide a complete multi-layer understanding of the performance of mobile applications and services from the user level, to the radio access through all the stack protocols.

The proliferation of mobile Internet services for smartphones makes it more necessary than ever to use measurement tools in mobile devices to monitor their connectivity performance. In this paper we introduce TestelDroid, a software tool for Android devices which provides advanced monitoring functionalities for analyzing multimedia services and applications specifically designed for smartphones. The beneficiaries of this tool are mobile operators, service providers and mobile developers, as TestelDroid offers a unified and independent methodology to analyze performance parameters from the application level, taking into account underlying issues.

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

In this paper we present a study of the performance of handovers for live GPRS, UMTS and HSDPA networks from the point of view of data traffic. Specifically, our analysis focuses on an evaluation of the impact of handover at IP level during data connections (packet losses, jitter and outage duration). The results provided in this paper have been obtained from extensive field tests carried out on European commercial mobile networks.

In this paper, we study the adequacy of applying peer-to- peer techniques to mobile networks by conducting a series of experiments using smart phones as peers. We measure important parameters, such as jitter and packet losses, in static and dynamic scenarios, focusing on a video streaming service. Finally, based on the results obtained, we discuss the feasibility of these applications.

In this paper, we focus on analyzing video streaming service performance on real networks. We propose a non intrusive methodology based on mobile devices as clients, instead of using them as modems. Our objective is to provide a more realistic test environment using actual mobile devices in real conditions of network load and radio propagation while taking into account the intrinsic mobility of mobile subscribers. This solution allows us to follow the end to end performance even when handover between different access technologies is performed. Using this methodology we carry out a study of video streaming behavior over GPRS and UMTS networks. Outstanding results related with delays, jitter, lost packets and sequence errors have been obtained. Also other conclusions about video quality, such as PSNR, have been achieved. Moreover, we analyze the impact of mobility issues such as handover or cell reselection.