Heterogeneous Cloud Radio Access Networks: A New Perspective for Enhancing Spectral and Energy Efficiencies

To mitigate the severe inter-tier interference and enhance limited cooperative gains resulting from the constrained and non-ideal transmissions between adjacent base stations in heterogeneous networks (HetNets), heterogeneous cloud radio access networks (H-CRANs) are proposed as cost-efficient potential solutions through incorporating the cloud computing into HetNets. In this article, state-of-the-art research achievements and challenges on H-CRANs are surveyed. In particular, we discuss issues of system architectures, spectral and energy efficiency performances, and promising key techniques. A great emphasis is given towards promising key techniques in H-CRANs to improve both spectral and energy efficiencies, including cloud computing based coordinated multi-point transmission and reception, large-scale cooperative multiple antenna, cloud computing based cooperative radio resource management, and cloud computing based self-organizing network in the cloud converging scenarios. The major challenges and open issues in terms of theoretical performance with stochastic geometry, fronthaul constrained resource allocation, and standard development that may block the promotion of H-CRANs are discussed as well.Comment: 20 pages, 6 figures, to be published in IEEE Wireless Communication

Cost-Effective Cache Deployment in Mobile Heterogeneous Networks

This paper investigates one of the fundamental issues in cache-enabled heterogeneous networks (HetNets): how many cache instances should be deployed at different base stations, in order to provide guaranteed service in a cost-effective manner. Specifically, we consider two-tier HetNets with hierarchical caching, where the most popular files are cached at small cell base stations (SBSs) while the less popular ones are cached at macro base stations (MBSs). For a given network cache deployment budget, the cache sizes for MBSs and SBSs are optimized to maximize network capacity while satisfying the file transmission rate requirements. As cache sizes of MBSs and SBSs affect the traffic load distribution, inter-tier traffic steering is also employed for load balancing. Based on stochastic geometry analysis, the optimal cache sizes for MBSs and SBSs are obtained, which are threshold-based with respect to cache budget in the networks constrained by SBS backhauls. Simulation results are provided to evaluate the proposed schemes and demonstrate the applications in cost-effective network deployment

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster

Energy Efficient User Association and Power Allocation in Millimeter Wave Based Ultra Dense Networks with Energy Harvesting Base Stations

Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem is modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and the effectiveness of the proposed scheme compared with existing methods is verified by simulations.Comment: to appear, IEEE Journal on Selected Areas in Communications, 201

Power Allocation for Massive MIMO-based, Fronthaul-constrained Cloud RAN Systems

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies to meet the diverse and stringent requirements of the 5G use cases. In a C-RAN system with massive MIMO, fronthaul is often the bottleneck due to its finite capacity and transmit precoding is moved to the remote radio head to reduce the capacity requirements on fronthaul. For such a system, we optimize the power allocated to the users to maximize first the weighted sum rate and then the energy efficiency (EE) while explicitly incorporating the capacity constraints on fronthaul. We consider two different fronthaul constraints, which model capacity constraints on different parts of the fronthaul network. We develop successive convex approximation algorithms that achieve a stationary point of these non-convex problems. To this end, we first present novel, locally tight bounds for the user rate expression. They are used to obtain convex approximations of the original non-convex problems, which are then solved by solving their dual problems. In EE maximization, we also employ the Dinkelbach algorithm to handle the fractional form of the objective function. Numerical results show that the proposed algorithms significantly improve the network performance compared to a case with no power control and achieves a better performance than an existing algorithm

Air-Ground Integrated Mobile Edge Networks: Architecture, Challenges and Opportunities

The ever-increasing mobile data demands have posed significant challenges in the current radio access networks, while the emerging computation-heavy Internet of things (IoT) applications with varied requirements demand more flexibility and resilience from the cloud/edge computing architecture. In this article, to address the issues, we propose a novel air-ground integrated mobile edge network (AGMEN), where UAVs are flexibly deployed and scheduled, and assist the communication, caching, and computing of the edge network. In specific, we present the detailed architecture of AGMEN, and investigate the benefits and application scenarios of drone-cells, and UAV-assisted edge caching and computing. Furthermore, the challenging issues in AGMEN are discussed, and potential research directions are highlighted.Comment: Accepted by IEEE Communications Magazine. 5 figure

Dynamic Joint Uplink and Downlink Optimization for Uplink and Downlink Decoupling-Enabled 5G Heterogeneous Networks

The concept of user-centric and personalized service in the fifth generation (5G) mobile networks encourages technical solutions such as dynamic asymmetric uplink/downlink resource allocation and elastic association of cells to users with decoupled uplink and downlink (DeUD) access. In this paper we develop a joint uplink and downlink optimization algorithm for DeUD-enabled wireless networks for adaptive joint uplink and downlink bandwidth allocation and power control, under different link association policies. Based on a general model of inter-cell interference, we propose a three-step optimization algorithm to jointly optimize the uplink and downlink bandwidth allocation and power control, using the fixed point approach for nonlinear operators with or without monotonicity, to maximize the minimum level of quality of service satisfaction per link, subjected to a general class of resource (power and bandwidth) constraints. We present numerical results illustrating the theoretical findings for network simulator in a real-world setting, and show the advantage of our solution compared to the conventional proportional fairness resource allocation schemes in both the coupled uplink and downlink (CoUD) access and the novel link association schemes in DeUD.Comment: 17 pages, 8 figure

Traffic offloading in future, heterogeneous mobile networks

The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. In order to tackle this challenge, Mobile Network Operators (MNOs) aim to increase their networks' capacity by expanding their infrastructure, deploying more Base Stations (BSs). Particularly, the creation of Heterogeneous Networks (HetNets) and the application of traffic offloading through the dense deployment of low-power BSs, the small cells (SCs), is one promising solution to address the aforementioned explosive data traffic increase. Due to their financial implementation requirements, which could not be met by the MNOs, the emergence of third parties that deploy small cell networks creates new business opportunities. Thus, the investigation of frameworks that facilitate the implementation of outsourced traffic offloading, the collaboration and the transactions among MNOs and third-party small cell owners, as well as the provision of participation incentives for all stakeholders is essential for the deployment of the necessary new infrastructure and capacity expansion. The aforementioned emergence of third-party content providers and their applications not only drives the increase in mobile data traffic, but also create new Quality of Service (QoS) as well as Quality of Experience (QoE) requirements that the MNOs need to guarantee for the satisfaction of their subscribers. Moreover, even though the MNOs accommodate this traffic, they do not get any monetary compensation or subsidization for the required capacity expansion. On the contrary, their revenues reduce continuously. To that end, it is necessary to research and design network and economic functionalities adapted to the new requirements, such as QoE-aware Radio Resource Management and Dynamic Pricing (DP) strategies, which both guarantee the subscriber satisfaction and maximization the MNO profit (to compensate the diminished MNOs' revenues and the increasing deployment investment). Following a thorough investigation of the state-of-the-art, a set of research directions were identified. This dissertation consists of contributions on network sharing and outsourced traffic offloading for the capacity enhancement of MNO networks, and the design of network and economic functions for the sustainable deployment and use of the densely constructed HetNets. The contributions of this thesis are divided into two main parts, as described in the following. The first part of the thesis introduces an innovative approach on outsourced traffic offloading, where we present a framework for the Multi-Operator Radio Access Network (MORAN) sharing. The proposed framework is based on an auction scheme used by a monopolistic Small Cell Operator (SCO), through which he leases his SC infrastructure to MNOs. As the lack of information on the future offered load and the auction strategies creates uncertainty for the MNOs, we designed a learning mechanism that assists the MNOs in their bid-placing decisions. Our simulations show that our proposal almost maximizes the social welfare, satisfying the involved stakeholders and providing them with participation incentives. The second part of the thesis researches the use of network and economic functions for MNO profit maximization, while guaranteeing the users' satisfaction. Particularly, we designed a model that accommodates a plethora of services with various QoS and QoE requirements, as well as diverse pricing, that is, various service prices and different charging schemes. In this model, we proposed QoE-aware user association, resource allocation and joint resource allocation and dynamic pricing algorithms, which exploit the QoE-awareness and the network's economic aspects, such as the profit. Our simulations have shown that our proposals gain substantial more profit compared to traditional and state-of-the-art solutions, while providing a similar or even better network performance.El aumento de los proveedores de contenido de terceros y la introducción de numerosas aplicaciones ha impulsado el crecimiento del tráfico de datos en redes móviles en los últimos años. Para hacer frente a este desafío, los operadores de redes móviles (Mobile Network Operators, MNOs) apuntan a aumentar la capacidad de sus redes mediante la expansión de su infraestructura y el despliegue de más estaciones base (BS). Particularmente, la creación de Redes Heterogéneas (Heterogenous Networks, HetNets) y la aplicación de descarga de tráfico a través del despliegue denso de BSs de baja potencia, las células pequeñas (small cells, SCs), es una solución prometedora para abordar el aumento del tráfico de datos explosivos antes mencionado. Debido a sus requisitos de implementación financiera, que los MNO no pudieron cumplir, la aparición de terceros que implementan redes de células pequeñas crea nuevas oportunidades comerciales. Por lo tanto, la investigación de marcos que faciliten la implementación de la descarga tercerizada de tráfico, la colaboración y las transacciones entre MNOs y terceros propietarios de células pequeñas, así como la provisión de incentivos de participación para todas las partes interesadas esencial para el despliegue de la nueva infraestructura necesaria y la expansión de la capacidad. La aparición antes mencionada de proveedores de contenido de terceros y sus aplicaciones no solo impulsa el aumento del tráfico de datos móviles, sino también crea nuevos requisitos de calidad de servicio (Quality of Service, QoS) y calidad de la experiencia (Quality of Experience, QoE) que los operadores de redes móviles deben garantizar para la satisfacción de sus suscriptores. Además, a pesar de que los operadores de redes móviles adaptan este tráfico, no obtienen ninguna compensación monetaria o subsidio por la expansión de capacidad requerida. Por el contrario, sus ingresos se reducen continuamente. Para ello, es necesario investigar y diseñar funcionalidades económicas y de red adaptadas a los nuevos requisitos, tales como las estrategias QoE-conscientes de gestión de recursos de radio y de precios dinámicos (Dynamic Pricing, DP), que garantizan la satisfacción del abonado y la maximización de la ganancia de operador móvil (para compensar los ingresos de los MNOs disminuidos y la creciente inversión de implementación). Después de una investigación exhaustiva del estado del arte, se identificaron un conjunto de direcciones de investigación. Esta disertación consiste en contribuciones sobre el uso compartido de redes y la descarga tercerizada de tráfico para la mejora de la capacidad de redes MNO, y el diseño de funciones económicas y de red para el despliegue y uso sostenible de las HetNets densamente construidas. Las contribuciones de esta tesis se dividen en dos partes principales, como se describe a continuación. La primera parte de la tesis presenta un enfoque innovador sobre la descarga subcontratada de tráfico, en el que presentamos un marco para el uso compartido de la red de acceso de radio de múltiples operadores (Multi-Operator RAN, MORAN). El marco propuesto se basa en un esquema de subasta utilizado por un operador monopólico de celda pequeña (Small Cell Operator, SCO), a través del cual arrienda su infraestructura SC a MNOs. Como la falta de información sobre la futura carga de red y las estrategias de subasta creaban incertidumbre para los MNO, diseñamos un mecanismo de aprendizaje que asiste a los MNO en sus decisiones de colocación de pujas. Nuestras simulaciones muestran que nuestra propuesta casi maximiza el bienestar social, satisfaciendo a las partes interesadas involucradas y proporcionándoles incentivos de participación. La segunda parte de la tesis investiga el uso de las funciones económicas y de red para la maximización de los beneficios de los MNOs, al tiempo que garantiza la satisfacción de los usuarios. Particularmente, diseñamos un modelo que acomoda una gran cantidad de servicios con diversos requisitos de QoS y QoE, tanto como diversos precios, es decir, varios precios de servicio y diferentes esquemas de cobro. En este modelo, propusimos algoritmos QoE-conscientes para asociación de usuarios, asignación de recursos y conjunta asignación de recursos y de fijación dinámica de precios, que explotan la conciencia de QoE y los aspectos económicos de la red, como la ganancia. Nuestras simulaciones han demostrado que nuestras propuestas obtienen un beneficio sustancial en comparación con las soluciones tradicionales y del estado del arte, a la vez que proporcionan un rendimiento de red similar o incluso mejor.Postprint (published version

Small Cell Offloading Through Cooperative Communication in Software-Defined Heterogeneous Networks

To meet the ever-growing demand for a higher communicating rate and better communication quality, more and more small cells are overlaid under the macro base station (MBS) tier, thus forming the heterogeneous networks. Small cells can ease the load pressure of MBS but lack of the guarantee of performance. On the other hand, cooperation draws more and more attention because of the great potential of small cell densification. Some technologies matured in wired network can also be applied to cellular networks, such as Software-defined networking (SDN). SDN helps simplify the structure of multi-tier networks. And it's more reasonable for the SDN controller to implement cell coordination. In this paper, we propose a method to offload users from MBSs through small cell cooperation in heterogeneous networks. Association probability is the main indicator of offloading. By using the tools from stochastic geometry, we then obtain the coverage probabilities when users are associated with different types of base stations (BSs). All the cell association and cooperation are conducted by the SDN controller. Then on this basis, we compare the overall coverage probabilities, achievable rate and energy efficiency with and without cooperation. Numerical results show that small cell cooperation can offload more users from MBS tier. It can also increase the system's coverage performance. As small cells become denser, cooperation can bring more gains to the energy efficiency of the network.Comment: 12 pages, 7 figure