Planning and Optimizing Mobile Backhaul for LTE

Lilius, Raija; Salo, J.; Pérez, José Manuel Tapia; Metsälä, Esa

doi:10.1002/9781118924655.ch5

Cited by 6 publications

(6 citation statements)

References 6 publications

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“…Packet loss: In most cases and as Equation 7assumes, packet loss in the backhaul network is generally negligible. This owes to the fact that, in commercial deployments, backhaul link dimensioning is done in such a way that the peak data rate or at least the average data rate of the cell is supported [34]. After all, if the backhaul link limits the cell data rate, there would be no point for the operator to invest in large frequency spectrum.…”

Section: Resultsmentioning

confidence: 99%

“…It is important to detail the three reasons that can prevent the flow from reaching this expected throughput: (1) the flow has a low demand (e.g., Web browsing flow); (2) the capacity of the backhaul is less than the current radio capacity allocated to the UE; (3) one or several concurrent data traffic (most likely unintercepted) exceed the capacity of the backhaul link. Suppose we are using a sheer download TCP flow, in this case we can eliminate (1) but also (2) since this condition is never met in commercial networks [34]. Likewise, the likelihood of observing (3) is close to zero in case of Constant-Bit-Rate (CBR) UDP flows such as Voice over IP (VoIP) traffic since their requirements in terms of bandwidth are very low (e.g., from 64 to few hundreds 𝑘𝑏𝑝𝑠 [41]).…”

Section: Experimentation and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

RAPID: A RAN-aware performance enhancing proxy for high throughput low delay flows in MEC-enabled cellular networks

Diarra

Dabbous²,

Ismail³

et al. 2022

Computer Networks

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Section: Resultsmentioning

confidence: 99%

Section: Experimentation and Resultsmentioning

confidence: 99%

RAPID: A RAN-aware performance enhancing proxy for high throughput low delay flows in MEC-enabled cellular networks

Diarra

Dabbous²,

Ismail³

et al. 2022

Computer Networks

View full text Add to dashboard Cite

“…Packet loss: In most cases and as Equation 5 assumes, packet loss in the backhaul network is generally negligible. This owes to the fact that, in commercial deployments, backhaul link dimensioning is done in such a way that the peak data rate or at least the average data rate of the cell is supported [21]. After all, if the backhaul link limits the cell data rate, there would be no point for the operator to invest in large frequency spectrum.…”

Section: Resultsmentioning

confidence: 99%

RAN-aware Proxy-based Flow Control for High Throughput and Low Delay eMBB

Diarra

Dabbous

Ismail³

et al. 2021

Proceedings of the 24th International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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“…The BH transports data between the aggregation point and the SCs, but it must also exchange control signals [119]. Let us characterize the signaling between the distributed SC b and the CN by its arrival rate, denoted by λ I b , and the average signaling packet size d I (i.e., the mean size of the signaling packets is assumed to be equal in all distributed SCs).…”

Section: Backhaul Characterizationmentioning

confidence: 99%

“…However, only loose upper and lower bounds for the performance metrics are known for G/G/1 queueing systems (i.e., general packet arrival and size distributions). Thus, for the sake of simplicity and according to [119],…”

Section: Backhaul Characterizationmentioning

confidence: 99%

Network virtualization in next generation cellular networks

Tseliou

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The complexity of operation and management of emerging cellular networks significantly increases, as they evolve to correspond to increasing QoS needs, data rates and diversity of offered services. Thus critical challenges appear regarding their performance. At the same time, network sustainability pushes toward the utilization of haring Radio Access Network (RAN) infrastructure between Mobile Network Operators (MNOs). This requires advanced network management techniques which have to be developed based on characteristics of these networks and traffic demands. Therefore it is necessary to provide solutions enabling the creation of logically isolated network partitions over shared physical network infrastructure. Multiple heterogeneous virtual networks should simultaneously coexist and support resource aggregation so as to appear as a single resource to serve different traffic types on demand. Hence in this thesis, we study RAN virtualization and slicing solutions destined to tackle these challenges. In the first part, we present our approach to map virtual network elements onto radio resources of the substrate physical network, in a dense multi-tier LTE-A scenario owned by a MNO. We propose a virtualization solution at BS level, where baseband modules of distributed BSs, interconnected via logical point-to-point X2 interface, cooperate to reallocate radio resources on a traffic need basis. Our proposal enhances system performance by achieving 53% throughput gain compared with benchmark schemes without substantial signaling overhead. In the second part of the thesis, we concentrate on facilitating resource provisioning between multiple Virtual MNOs (MVNOs), by integrating the capacity broker in the 3GPP network management architecture with minimum set of enhancements. A MNO owns the network and provides RAN access on demand to several MVNOs. Furthermore we propose an algorithm for on-demand resource allocation considering two types of traffic. Our proposal achieves 50% more admitted requests without Service Level Agreement (SLA) violation compared with benchmark schemes. In the third part, we devise and study a solution for BS agnostic network slicing leveraging BS virtualization in a multi-tenant scenario. This scenario is composed of different traffic types (e.g., tight latency requirements and high data rate demands) along with BSs characterized by different access and transport capabilities (i.e., Remote Radio Heads, RRHs, Small Cells, SCs and future 5G NodeBs, gNBs with various functional splits having ideal and non-ideal transport network). Our solution achieves 67% average spectrum usage gain and 16.6% Baseband Unit processing load reduction compared with baseline scenarios. Finally, we conclude the thesis by providing insightful research challenges for future works. La complejidad de la operación y la gestión de las emergentes redes celulares aumenta a medida que evolucionan para hacer frente a las crecientes necesidades de calidad de servicio (QoS), las tasas de datos y la diversidad de los servicios ofrecidos. De esta forma aparecen desafíos críticos con respecto a su rendimiento. Al mismo tiempo, la sostenibilidad de la red empuja hacia la utilización de la infraestructura de red de acceso radio (RAN) compartida entre operadores de redes móviles (MNO). Esto requiere técnicas avanzadas de gestión de redes que deben desarrollarse en función de las características especiales de estas redes y las demandas de tráfico. Por lo tanto, es necesario proporcionar soluciones que permitan la creación de particiones de red aisladas lógicamente sobre la infraestructura de red física compartida. Para ello, en esta tesis, estudiamos las soluciones de virtualización de la RAN destinadas a abordar estos desafíos. En la primera parte de la tesis, nos centramos en mapear elementos de red virtual en recursos de radio de la red física, en un escenario LTE-A de múltiples niveles que es propiedad de un solo MNO. Proponemos una solución de virtualización a nivel de estación base (BS), donde los módulos de banda base de BSs distribuidas, interconectadas a través de la interfaz lógica X2, cooperan para reasignar los recursos radio en función de las necesidades de tráfico. Nuestra propuesta mejora el rendimiento del sistema al obtener un rendimiento 53% en comparación con esquemas de referencia. En la segunda parte de la tesis, nos concentramos en facilitar el aprovisionamiento de recursos entre muchos operadores de redes virtuales móviles (MVNO), al integrar el capacity broker en la arquitectura de administración de red 3GPP con un conjunto míinimo de mejoras. En este escenario, un MNO es el propietario de la red y proporciona acceso bajo demanda (en inglés on-demand) a varios MVNOs. Además, para aprovechar al máximo las capacidades del capacity broker, proponemos un algoritmo para la asignación de recursos bajo demanda, considerando dos tipos de tráfico con distintas características. Nuestra propuesta alcanza 50% más de solicitudes admitidas sin violación del Acuerdo de Nivel de Servicio (SLA) en comparación con otros esquemas. En la tercera parte de la tesis, estudiamos una solución para el slicing de red independiente del tipo de BS, considerando la virtualización de BS en un escenario de múltiples MVNOs (multi-tenants). Este escenario se compone de diferentes tipos de tráfico (por ejemplo, usuarios con requisitos de latencia estrictos y usuarios con altas demandas de velocidad de datos) junto con BSs caracterizadas por diferentes capacidades de acceso y transporte (por ejemplo, Remote Radio Heads, RRHs, Small cells, SC y 5G NodeBs, gNBs con varias divisiones funcionales que tienen una red de transporte ideal y no ideal). Nuestra solución logra una ganancia promedio de uso de espectro de 67% y una reducción de la carga de procesamiento de la banda base de 16.6% en comparación con escenarios de referencia. Finalmente, concluimos la tesis al proporcionando los desafíos y retos de investigación para trabajos futuros.

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Planning and Optimizing Mobile Backhaul for LTE

Cited by 6 publications

References 6 publications

RAPID: A RAN-aware performance enhancing proxy for high throughput low delay flows in MEC-enabled cellular networks

RAPID: A RAN-aware performance enhancing proxy for high throughput low delay flows in MEC-enabled cellular networks

RAN-aware Proxy-based Flow Control for High Throughput and Low Delay eMBB

Network virtualization in next generation cellular networks

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