224 research outputs found
Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks
Soaring capacity and coverage demands dictate that future cellular networks
need to soon migrate towards ultra-dense networks. However, network
densification comes with a host of challenges that include compromised energy
efficiency, complex interference management, cumbersome mobility management,
burdensome signaling overheads and higher backhaul costs. Interestingly, most
of the problems, that beleaguer network densification, stem from legacy
networks' one common feature i.e., tight coupling between the control and data
planes regardless of their degree of heterogeneity and cell density.
Consequently, in wake of 5G, control and data planes separation architecture
(SARC) has recently been conceived as a promising paradigm that has potential
to address most of aforementioned challenges. In this article, we review
various proposals that have been presented in literature so far to enable SARC.
More specifically, we analyze how and to what degree various SARC proposals
address the four main challenges in network densification namely: energy
efficiency, system level capacity maximization, interference management and
mobility management. We then focus on two salient features of future cellular
networks that have not yet been adapted in legacy networks at wide scale and
thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and
device-to-device (D2D) communications. After providing necessary background on
CoMP and D2D, we analyze how SARC can particularly act as a major enabler for
CoMP and D2D in context of 5G. This article thus serves as both a tutorial as
well as an up to date survey on SARC, CoMP and D2D. Most importantly, the
article provides an extensive outlook of challenges and opportunities that lie
at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201
Techno-Economic Analysis of 5G Deployment Scenarios involving Massive MIMO HetNets over mmWave: A Case Study on the US State of Texas
The fifth generation (5G) of mobile services envisages network heterogeneity, cell densification, and high spectral efficiency using Massive MIMO, operating at millimeter-wave frequencies. Accurately assessing the potential of financial returns for such a complex network poses to operators unique challenges including techno-economic analysis leading to the identification of decision variables most sensitive to the profitability parameters. Attempting to demystify their concerns, we evaluate the profitability potential for realistic 5G deployment scenarios over 28 GHz frequency in the State of Texas. Interestingly, we discover that the total cost of ownership for 5G network is about one-third of that for 4G LTE-Advanced (LTE-A) deployment, yielding estimated returns amounting to $482.14 million for the period 2020-2030. The sensitivity analyses predict profitability in 70% of the cases of 5G, against LTE-A. For operators, the crucial levers having the maximum impact on profitability are decisions pertaining to the spectrum acquisition and the pricing of services
Energy sustainability of next generation cellular networks through learning techniques
The trend for the next generation of cellular network, the Fifth Generation (5G), predicts a 1000x increase in the capacity demand with respect to 4G, which leads to new infrastructure deployments. To this respect, it is estimated that the energy consumption of ICT might reach the 51% of global electricity production by 2030, mainly due to mobile networks and services. Consequently, the cost of energy may also become predominant in the operative expenses of a Mobile Network Operator (MNO). Therefore, an efficient control of the energy consumption in 5G networks is not only desirable but essential. In fact, the energy sustainability is one of the pillars in the design of the next generation cellular networks.
In the last decade, the research community has been paying close attention to the Energy Efficiency (EE) of the radio communication networks, with particular care on the dynamic switch ON/OFF of the Base Stations (BSs). Besides, 5G architectures will introduce the Heterogeneous Network (HetNet) paradigm, where Small BSs (SBSs) are deployed to assist the standard macro BS for satisfying the high traffic demand and reducing the impact on the energy consumption. However, only with the introduction of Energy Harvesting (EH) capabilities the networks might reach the needed energy savings for mitigating both the high costs and the environmental impact. In the case of HetNets with EH capabilities, the erratic and intermittent nature of renewable energy sources has to be considered, which entails some additional complexity. Solar energy has been chosen as reference EH source due to its widespread adoption and its high efficiency in terms of energy produced compared to its costs. To this end, in the first part of the thesis, a harvested solar energy model has been presented based on accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources.
The typical HetNet scenario involves dense deployments with a high level of flexibility, which suggests the usage of distributed control systems rather than centralized, where the scalability can become rapidly a bottleneck. For this reason, in the second part of the thesis, we propose to model the SBS tier as a Multi-agent Reinforcement Learning (MRL) system, where each SBS is an intelligent and autonomous agent, which learns by directly interacting with the environment and by properly utilizing the past experience. The agents implemented in each SBS independently learn a proper switch ON/OFF control policy, so as to jointly maximize the system performance in terms of throughput, drop rate and energy consumption, while adapting to the dynamic conditions of the environment, in terms of energy inflow and traffic demand.
However, MRL might suffer the problem of coordination when finding simultaneously a solution among all the agents that is good for the whole system. In consequence, the Layered Learning paradigm has been adopted to simplify the problem by decomposing it in subtasks. In particular, the global solution is obtained in a hierarchical fashion: the learning process of a subtask is aimed at facilitating the learning of the next higher subtask layer. The first layer implements an MRL approach and it is in charge of the local online optimization at SBS level as function of the traffic demand and the energy incomes. The second layer is in charge of the network-wide optimization and it is based on Artificial Neural Networks aimed at estimating the model of the overall network.Con la llegada de la nueva generación de redes móviles, la quinta generación (5G), se predice un aumento por un factor 1000 en la demanda de capacidad respecto a la 4G, con la consecuente instalación de nuevas infraestructuras. Se estima que el gasto energético de las tecnologías de la información y la comunicación podría alcanzar el 51% de la producción mundial de energía en el año 2030, principalmente debido al impacto de las redes y servicios móviles. Consecuentemente, los costes relacionados con el consumo de energía pasarán a ser una componente predominante en los gastos operativos (OPEX) de las operadoras de redes móviles. Por lo tanto, un control eficiente del consumo energético de las redes 5G, ya no es simplemente deseable, sino esencial. En la última década, la comunidad científica ha enfocado sus esfuerzos en la eficiencia energética (EE) de las redes de comunicaciones móviles, con particular énfasis en algoritmos para apagar y encender las estaciones base (BS). Además, las arquitecturas 5G introducirán el paradigma de las redes heterogéneas (HetNet), donde pequeñas BSs, o small BSs (SBSs), serán desplegadas para ayudar a las grandes macro BSs en satisfacer la gran demanda de tráfico y reducir el impacto en el consumo energético. Sin embargo, solo con la introducción de técnicas de captación de la energía ambiental, las redes pueden alcanzar los ahorros energéticos requeridos para mitigar los altos costes de la energía y su impacto en el medio ambiente. En el caso de las HetNets alimentadas mediante energías renovables, la naturaleza errática e intermitente de esta tipología de energías constituye una complejidad añadida al problema. La energía solar ha sido utilizada como referencia debido a su gran implantación y su alta eficiencia en términos de cantidad de energía producida respecto costes de producción. Por consiguiente, en la primera parte de la tesis se presenta un modelo de captación de la energía solar basado en un riguroso modelo estocástico de Markov que representa la energía capturada por paneles solares para exteriores. El escenario típico de HetNet supondrá el despliegue denso de SBSs con un alto nivel de flexibilidad, lo cual sugiere la utilización de sistemas de control distribuidos en lugar de aquellos que están centralizados, donde la adaptabilidad podría convertirse rápidamente en un reto difícilmente gestionable. Por esta razón, en la segunda parte de la tesis proponemos modelar las SBSs como un sistema multiagente de aprendizaje automático por refuerzo, donde cada SBS es un agente inteligente y autónomo que aprende interactuando directamente con su entorno y utilizando su experiencia acumulada. Los agentes en cada SBS aprenden independientemente políticas de control del apagado y encendido que les permiten maximizar conjuntamente el rendimiento y el consumo energético a nivel de sistema, adaptándose a condiciones dinámicas del ambiente tales como la energía renovable entrante y la demanda de tráfico. No obstante, los sistemas multiagente sufren problemas de coordinación cuando tienen que hallar simultáneamente una solución de forma distribuida que sea buena para todo el sistema. A tal efecto, el paradigma de aprendizaje por niveles ha sido utilizado para simplificar el problema dividiéndolo en subtareas. Más detalladamente, la solución global se consigue de forma jerárquica: el proceso de aprendizaje de una subtarea está dirigido a ayudar al aprendizaje de la subtarea del nivel superior. El primer nivel contempla un sistema multiagente de aprendizaje automático por refuerzo y se encarga de la optimización en línea de las SBSs en función de la demanda de tráfico y de la energía entrante. El segundo nivel se encarga de la optimización a nivel de red del sistema y está basado en redes neuronales artificiales diseñadas para estimar el modelo de todas las BSsPostprint (published version
Eficiência energética avançada para sistema OFDMA CoMP coordenação multiponto
Doutoramento em Engenharia EletrotécnicaThe ever-growing energy consumption in mobile networks stimulated by
the expected growth in data tra ffic has provided the impetus for mobile
operators to refocus network design, planning and deployment towards reducing
the cost per bit, whilst at the same time providing a signifi cant step
towards reducing their operational expenditure. As a step towards incorporating
cost-eff ective mobile system, 3GPP LTE-Advanced has adopted the
coordinated multi-point (CoMP) transmission technique due to its ability
to mitigate and manage inter-cell interference (ICI). Using CoMP the cell
average and cell edge throughput are boosted. However, there is room for
reducing energy consumption further by exploiting the inherent
exibility of
dynamic resource allocation protocols. To this end packet scheduler plays
the central role in determining the overall performance of the 3GPP longterm
evolution (LTE) based on packet-switching operation and provide a
potential research playground for optimizing energy consumption in future
networks. In this thesis we investigate the baseline performance for down
link CoMP using traditional scheduling approaches, and subsequently go
beyond and propose novel energy e fficient scheduling (EES) strategies that
can achieve power-e fficient transmission to the UEs whilst enabling both
system energy effi ciency gain and fairness improvement. However, ICI can
still be prominent when multiple nodes use common resources with di fferent
power levels inside the cell, as in the so called heterogeneous networks (Het-
Net) environment. HetNets are comprised of two or more tiers of cells. The
rst, or higher tier, is a traditional deployment of cell sites, often referred
to in this context as macrocells. The lower tiers are termed small cells, and
can appear as microcell, picocells or femtocells. The HetNet has attracted
signiffi cant interest by key manufacturers as one of the enablers for high
speed data at low cost. Research until now has revealed several key hurdles
that must be overcome before HetNets can achieve their full potential:
bottlenecks in the backhaul must be alleviated, as well as their seamless
interworking with CoMP. In this thesis we explore exactly the latter hurdle,
and present innovative ideas on advancing CoMP to work in synergy with
HetNet deployment, complemented by a novel resource allocation policy
for HetNet tighter interference management. As system level simulator has
been used to analyze the proposed algorithm/protocols, and results have
concluded that up to 20% energy gain can be observed.O aumento do consumo de energia nas TICs e em particular nas redes de
comunicação móveis, estimulado por um crescimento esperado do tráfego de
dados, tem servido de impulso aos operadores m oveis para reorientarem os
seus projectos de rede, planeamento e implementa ção no sentido de reduzir
o custo por bit, o que ao mesmo tempo possibilita um passo signicativo no
sentido de reduzir as despesas operacionais. Como um passo no sentido de
uma incorporação eficaz em termos destes custos, o sistema móvel 3GPP
LTE-Advanced adoptou a técnica de transmissão Coordenação Multi-Ponto
(identificada na literatura com a sigla CoMP) devido à sua capacidade de
mitigar e gerir Interferência entre Células (sigla ICI na literatura). No entanto
a ICI pode ainda ser mais proeminente quando v arios n os no interior
da célula utilizam recursos comuns com diferentes níveis de energia,
como acontece nos chamados ambientes de redes heterogéneas (sigla Het-
Net na literatura). As HetNets são constituídas por duas ou mais camadas
de células. A primeira, ou camada superiora, constitui uma implantação
tradicional de sítios de célula, muitas vezes referidas neste contexto como
macrocells. Os níveis mais baixos são designados por células pequenas, e
podem aparecer como microcells, picocells ou femtocells. A HetNet tem
atra do grande interesse por parte dos principais fabricantes como sendo
facilitador para transmissões de dados de alta velocidade a baixo custo. A
investigação tem revelado at e a data, vários dos principais obstáculos que
devem ser superados para que as HetNets possam atingir todo o seu potencial:
(i) os estrangulamentos no backhaul devem ser aliviados; (ii) bem
como sua perfeita interoperabilidade com CoMP. Nesta tese exploramos
este ultimo constrangimento e apresentamos ideias inovadoras em como a
t ecnica CoMP poder a ser aperfeiçoada por forma a trabalhar em sinergia
com a implementação da HetNet, complementado ainda com uma nova
perspectiva na alocação de recursos rádio para um controlo e gestão mais
apertado de interferência nas HetNets. Com recurso a simulação a níível de
sistema para analisar o desempenho dos algoritmos e protocolos propostos,
os resultados obtidos concluíram que ganhos at e a ordem dos 20% poderão
ser atingidos em termos de eficiência energética
Interference-Aware Downlink and Uplink Resource Allocation in HetNets with D2D Support
We address the resource allocation problem in an LTE-based 2-tier heterogeneous network where in-band D2D communications are supported under network control. The different communication paradigms share the same radio resources, thus they may interfere. We devise a dynamic programming approach to efficiently schedule download and upload traffic, by 1) efficiently matching communicating endpoints and 2) assigning radio resources in an interference-aware manner while accounting for the characteristics of the content to be delivered. To this end, we develop an accurate model of the system and apply approximate dynamic programming to solve it. Our solution allows us to deal with realistic large-scale scenarios. In such scenarios, we compare our approach to today's networks where eICIC techniques and proportional fairness scheduling are implemented. Results highlight that our solution increases the system throughput while greatly reducing energy consumption. We also show that D2D mode, established either in the downlink or uplink, can effectively support delivery of highly popular content without significantly harming macrocell or microcell traffic, leading to increased system capacity. Interestingly, we find that D2D mode can also be a low-cost alternative to microcells
CLEVER: a cooperative and cross-layer approach to video streaming in HetNets
We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (eNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of eNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After optimally formulating the problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer aswell as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE)
THROUGHPUT IMPROVEMENT AND COMPARATIVE PERFORMANCE ANALYSIS OF INTEGRATED NETWORKS
The demand for high-speed communication continue to increase significantly. Industry forecasts have shown that future data services would contribute to rapid growth in data traffic, with most of this traffic primarily indoors and at hotspots locations. Thus, the deployment and integration of small cell base stations (SCBSs) with Wireless Local Area Network (WLAN) or Wi-Fi is viewed as a critical solution to offload traffic, maximize coverage and boost future wireless systems capacity.
This thesis reviews the existing network of WLAN, Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). Tight and Loosely coupled integration of these networks is studied.
More specifically, the introduction of small cell (SC) in loosely coupled Wi-Fi /WiMAX and Wi-Fi/LTE are proposed. These designs are tested in real-time user experience applications consisting of video conferencing, hypertext transfer protocol (HTTP) and email using industrial simulation software, Riverbed Modeler 18.7.
Quality of service parameters was used to analyze these networks. It was found that the throughput of loosely coupled Wi-Fi/WiMAX network can be optimized by small cell. The loosely coupled architecture of Wi-Fi/WiMAX small cell outperforms that of Wi-Fi/LTE small cell. The loosely coupled independently deployed network of Wi-Fi/LTE small cell performs better than the Wi-Fi network. The Wi-Fi/LTE small cell network achieved a substantial rise in downlink throughput in a network consisting of only video conferencing subscriber station
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