A Comparative Study on the Quality of Narrow-Band and Wide-Band AMR VoLTE Calls

This work performs a comparative analysis of the end-to-end quality guaranteed by Voice over LTE (VoLTE), examining several millions of VoLTE calls that employ two popular speech audio codecs, namely, Adaptive Multi-Rate (AMR) and Adaptive Multi-Rate Wide Band (AMR-WB). To assess call quality, VQmon, an enhanced version of the standardized E-Model, is utilized. The study reveals to what extent AMRWB based calls are more robust against network impairments than their narrowband counterparts; it further shows that the dependence of call quality on the packet loss rate is approximately exponential for both types of codec

A methodology for obtaining More Realistic Cross-Layer QoS Measurements in mobile networks: A VoIP over LTE Use Case

Los servicios de voz han sido durante mucho tiempo la primera fuente de ingresos para los operadores móviles. Incluso con el protagonismo creciente del tráfico de datos, los servicios de voz seguirán jugando un papel importante y no desaparecerán con la transición a redes basadas en el protocolo IP. Por otra parte, hace años que los principales actores en la industria móvil detectaron claramente que los usuarios no aceptarían una degradación en la calidad de los servicios de voz. Es por esto que resulta crítico garantizar la experiencia de usuario (QoE) en la transición a redes de nueva generación basadas en conmutación de paquetes. El trabajo realizado durante esta tesis ha buscado analizar el comportamiento y las dependencias de los diferentes servicios de Voz sobre IP (VoIP), así como identificar configuraciones óptimas, mejoras potenciales y metodologías que permitan asegurar niveles de calidad aceptables al mismo tiempo que se trate de minimizar los costes. La caracterización del rendimiento del tráfico de datos en redes móviles desde el punto de vista de los usuarios finales es un proceso costoso que implica la monitorización y análisis de un amplio rango de protocolos y parámetros con complejas dependencias. Para abordar desde la raíz este problema, se requiere realizar medidas que relacionen y correlen el comportamiento de las diferentes capas. La metodología de caracterización propuesta en esta tesis proporciona la posibilidad de recoger información clave para la resolución de problemas en las comunicaciones IP, relaciolándola con efectos asociados a la propagación radio, como cambios de celda o pérdida de enlaces, o con carga de la red y limitaciones de recursos en zonas geográficas específicas. Dicha metodología se sustenta en la utilización de herramientas nativas de monitorización y registro de información en smartphones, y la aplicación de cadenas de herramientas para la experimentación extensiva tanto en redes reales y como en entornos de prueba controlados. Con los resultados proporcionados por esta serie de herramientas, tanto operadores móviles y proveedores de servicio como desarrolladores móviles podrían ganar acceso a información sobre la experiencia real del usuario y sobre cómo mejorar la cobertura, optimizar los servicios y adaptar el funcionamiento de las aplicaciones y el uso de protocolos móviles basados en IP en este contexto. Las principales contribuciones de las herramientas y métodos introducidos en esta tesis son los siguientes: - Una herramienta de monitorización multicapa para smartphones Android, llamada TestelDroid, que permite la captura de indicadores clave de rendimiento desde el propio equipo de usuario. Asimismo proporciona la capacidad de generar tráfico de forma activa y de verificar el estado de alcanzabilidad del terminal, realizando pruebas de conectividad. - Una metodología de post-procesado para correlar la información presente en las diferentes capas de las medidas realizadas. De igual forma, se proporciona la opción a los usuarios de acceder directamente a la información sobre el tráfico IP y las medidas radio y de aplicar metodologías propias para la obtención de métricas. - Se ha realizado la aplicación de la metodología y de las herramientas usando como caso de uso el estudio y evaluación del rendimiento de las comunicaciones basadas en IP a bordo de trenes de alta velocidad. - Se ha contribuido a la creación de un entorno de prueba realista y altamente configurable para la realización de experimentos avanzados sobre LTE. - Se han detectado posibles sinergias en la utilización de instrumentación avanzada de I+D en el campo de las comunicaciones móviles, tanto para la enseñanza como para la investigación en un entorno universitario

A novel non-intrusive objective method to predict voice quality of service in LTE networks.

This research aimed to introduce a novel approach for non-intrusive objective measurement of voice Quality of Service (QoS) in LTE networks. While achieving this aim, the thesis established a thorough knowledge of how voice traffic is handled in LTE networks, the LTE network architecture and its similarities and differences to its predecessors and traditional ground IP networks and most importantly those QoS affecting parameters which are exclusive to LTE environments. Mean Opinion Score (MOS) is the scoring system used to measure the QoS of voice traffic which can be measured subjectively (as originally intended). Subjective QoS measurement methods are costly and time-consuming, therefore, objective methods such as Perceptual Evaluation of Speech Quality (PESQ) were developed to address these limitations. These objective methods have a high correlation with subjective MOS scores. However, they either require individual calculation of many network parameters or have an intrusive nature that requires access to both the reference signal and the degraded signal for comparison by software. Therefore, the current objective methods are not suitable for application in real-time measurement and prediction scenarios. A major contribution of the research was identifying LTE-specific QoS affecting parameters. There is no previous work that combines these parameters to assess their impacts on QoS. The experiment was configured in a hardware in the loop environment. This configuration could serve as a platform for future research which requires simulation of voice traffic in LTE environments. The key contribution of this research is a novel non-intrusive objective method for QoS measurement and prediction using neural networks. A comparative analysis is presented that examines the performance of four neural network algorithms for non-intrusive measurement and prediction of voice quality over LTE networks. In conclusion, the Bayesian Regularization algorithm with 4 neurons in the hidden layer and sigmoid symmetric transfer function was identified as the best solution with a Mean Square Error (MSE) rate of 0.001 and regression value of 0.998 measured for the testing data set

Radio Resource Management Optimization For Next Generation Wireless Networks

The prominent versatility of today’s mobile broadband services and the rapid advancements in the cellular phones industry have led to a tremendous expansion in the wireless market volume. Despite the continuous progress in the radio-access technologies to cope with that expansion, many challenges still remain that need to be addressed by both the research and industrial sectors. One of the many remaining challenges is the efficient allocation and management of wireless network resources when using the latest cellular radio technologies (e.g., 4G). The importance of the problem stems from the scarcity of the wireless spectral resources, the large number of users sharing these resources, the dynamic behavior of generated traffic, and the stochastic nature of wireless channels. These limitations are further tightened as the provider’s commitment to high quality-of-service (QoS) levels especially data rate, delay and delay jitter besides the system’s spectral and energy efficiencies. In this dissertation, we strive to solve this problem by presenting novel cross-layer resource allocation schemes to address the efficient utilization of available resources versus QoS challenges using various optimization techniques. The main objective of this dissertation is to propose a new predictive resource allocation methodology using an agile ray tracing (RT) channel prediction approach. It is divided into two parts. The first part deals with the theoretical and implementational aspects of the ray tracing prediction model, and its validation. In the second part, a novel RT-based scheduling system within the evolving cloud radio access network (C-RAN) architecture is proposed. The impact of the proposed model on addressing the long term evolution (LTE) network limitations is then rigorously investigated in the form of optimization problems. The main contributions of this dissertation encompass the design of several heuristic solutions based on our novel RT-based scheduling model, developed to meet the aforementioned objectives while considering the co-existing limitations in the context of LTE networks. Both analytical and numerical methods are used within this thesis framework. Theoretical results are validated with numerical simulations. The obtained results demonstrate the effectiveness of our proposed solutions to meet the objectives subject to limitations and constraints compared to other published works

IoT Traffic Management and Integration in the QoS Supported Network

This paper proposes: (i) a traffic flow management policy, which allocates and organises Machine Type Communication (MTC) traffic flows network resources sharing within Evolved Packet System (EPS), (ii) an access element as a Wireless Sensor Network (WSN) gateway for providing an overlaying access channel between the Machine Type Devices (MTDs) and EPS and (iii) it addresses the effect and interaction in the heterogeneity of applications, services and terminal devices and the related Quality of Service (QoS) issues among them. This work overcomes the problems of network resource starvation by preventing deterioration of network performance. The scheme is validated through simulation, which indicates the proposed traffic flow management policy outperforms the current traffic management policy. Specifically, simulation results show that the proposed model achieves an enhancement in QoS performance for the MTC traffic flows, including a decrease of 99.45% in Packet Loss Ratio (PLR), a decrease of 99.89% in packet End To End (E2E) delay, a decrease of 99.21% in Packet Delay Variation (PDV). Furthermore, it retains the perceived Quality of Experience (QoE) of the real time application users within high satisfaction levels, such as the VoLTE service possessing a Mean Opinion Score (MOS) of 4.349 and enhancing the QoS of a video conference service within the standardised values of a 3GPP body, with a decrease of 85.28% in PLR, a decrease of 85% in packet E2E delay and a decrease of 88.5% in PDV

The Bits of Silence : Redundant Traffic in VoIP

Human conversation is characterized by brief pauses and so-called turn-taking behavior between the speakers. In the context of VoIP, this means that there are frequent periods where the microphone captures only background noise – or even silence whenever the microphone is muted. The bits transmitted from such silence periods introduce overhead in terms of data usage, energy consumption, and network infrastructure costs. In this paper, we contribute by shedding light on these costs for VoIP applications. We systematically measure the performance of six popular mobile VoIP applications with controlled human conversation and acoustic setup. Our analysis demonstrates that significant savings can indeed be achievable - with the best performing silence suppression technique being effective on 75% of silent pauses in the conversation in a quiet place. This results in 2-5 times data savings, and 50-90% lower energy consumption compared to the next better alternative. Even then, the effectiveness of silence suppression can be sensitive to the amount of background noise, underlying speech codec, and the device being used. The codec characteristics and performance do not depend on the network type. However, silence suppression makes VoIP traffic network friendly as much as VoLTE traffic. Our results provide new insights into VoIP performance and offer a motivation for further enhancements, such as performance-aware codec selection, that can significantly benefit a wide variety of voice assisted applications, as such intelligent home assistants and other speech codec enabled IoT devices.Peer reviewe