Joint User-Association and Resource-Allocation in Virtualized Wireless Networks

In this paper, we consider a down-link transmission of multicell virtualized wireless networks (VWNs) where users of different service providers (slices) within a specific region are served by a set of base stations (BSs) through orthogonal frequency division multiple access (OFDMA). In particular, we develop a joint BS assignment, sub-carrier and power allocation algorithm to maximize the network throughput, while satisfying the minimum required rate of each slice. Under the assumption that each user at each transmission instance can connect to no more than one BS, we introduce the user-association factor (UAF) to represent the joint sub-carrier and BS assignment as the optimization variable vector in the mathematical problem formulation. Sub-carrier reuse is allowed in different cells, but not within one cell. As the proposed optimization problem is inherently non-convex and NP-hard, by applying the successive convex approximation (SCA) and complementary geometric programming (CGP), we develop an efficient two-step iterative approach with low computational complexity to solve the proposed problem. For a given power-allocation, Step 1 derives the optimum userassociation and subsequently, for an obtained user-association, Step 2 find the optimum power-allocation. Simulation results demonstrate that the proposed iterative algorithm outperforms the traditional approach in which each user is assigned to the BS with the largest average value of signal strength, and then, joint sub-carrier and power allocation is obtained for the assigned users of each cell. Especially, for the cell-edge users, simulation results reveal a coverage improvement up to 57% and 71% for uniform and non-uniform users distribution, respectively leading to more reliable transmission and higher spectrum efficiency for VWN

Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Network virtualization in next generation cellular networks

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.Postprint (published version