Asymptotic Behavior of Ultra-Dense Cellular Networks and Its Economic Impact

This paper investigates the relationship between base station (BS) density and average spectral efficiency (SE) in the downlink of a cellular network. This relationship has been well known for sparse deployment, i.e. when the number of BSs is small compared to the number of users. In this case the SE is independent of BS density. As BS density grows, on the other hand, it has previously been shown that increasing the BS density increases the SE, but no tractable form for the SE-BS density relationship has yet been derived. In this paper we derive such a closed-form result that reveals the SE is asymptotically a logarithmic function of BS density as the density grows. Further, we study the impact of this result on the network operator's profit when user demand varies, and derive the profit maximizing BS density and the optimal amount of spectrum to be utilized in closed forms. In addition, we provide deployment planning guidelines that will aid the operator in his decision if he should invest in densifying his network or in acquiring more spectrum.Comment: This paper will appear in Proc. IEEE Global Commun. Conf. (GLOBECOM) 201

Content-Specific Broadcast Cellular Networks based on User Demand Prediction: A Revenue Perspective

The Long Term Evolution (LTE) broadcast is a promising solution to cope with exponentially increasing user traffic by broadcasting common user requests over the same frequency channels. In this paper, we propose a novel network framework provisioning broadcast and unicast services simultaneously. For each serving file to users, a cellular base station determines either to broadcast or unicast the file based on user demand prediction examining the file's content specific characteristics such as: file size, delay tolerance, price sensitivity. In a network operator's revenue maximization perspective while not inflicting any user payoff degradation, we jointly optimize resource allocation, pricing, and file scheduling. In accordance with the state of the art LTE specifications, the proposed network demonstrates up to 32% increase in revenue for a single cell and more than a 7-fold increase for a 7 cell coordinated LTE broadcast network, compared to the conventional unicast cellular networks.Comment: 6 pages; This paper will appear in the Proc. of IEEE WCNC 201

Providing adaptive traffic routing based on user and network context

Providing real-time traffic guarantees and fairness based on the availability of network resources has been a major issue presented in the literature. However, due the convergent nature of digital architectures, the increasing demand of upcoming real-time sensitive traffic, such as VoIP, and a higher user´s adaptability (devices, global positioning, content quality, etc.), solutions based on Quality of Service (QoS) turned out to be insufficient in order to meet user´s requirements. Indeed, QoS metrics are network-centered, and mostly related to the dynamic nature of the traffic (such as throughput, delay, jitter, among others). In order to meet the need for a user-centered network, this paper proposes a context-aware solution where the concepts of Quality of Service, Quality of Experience and Adaptive Routing are integrated in order to provide a more dynamic and pro-active approach for the delivery of context-oriented time-sensitive traffic.info:eu-repo/semantics/publishedVersio

Implementation of Quality of Experience Prediction Framework through Mobile Network Data

Generally, a reliable method of analyzing the quality of experience is through the subjective method, which is time consuming, lacks usability, lacks repeatability in real-time and near real-time. Another method is the objective measurement that aims at predicting the subjective measurement based on the estimated mean opinion score. Therefore, this study adopted the objective measurement by implementing a quality of experience framework, which employed predictive analytics techniques to analyze the mobile internet user experience dataset gathered through the mobile network. The predictive analytics employed the use of multiple regression, neural network, decision trees, random forest, and decision forest to predict the mobile internet perceived quality of experience. Result from the study shows that decision forests performs better than other algorithms used for the predictive analytics. In addition, the result indicates that the predictive analytics can be used to enhance the allocation of network resources based on location and time constituted in the dataset

A telecom analytics framework for dynamic quality of service management

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications

Network Performance Criteria for Telecommunication Traffic Types driven by Quality of Experience

A common reason for changing the chosen service provider is the users\u27 perception of service. Quality of Experience (QoE) describes the end user\u27s perception of service while using it. A frequent cause of QoE degradation is inadequate traffic routing, where, other than throughput, selected routes do not satisfy minimum network requirements for the given service or services. In order to enable QoE-driven routing, per traffic type defined routing criteria are required. Our goal was to obtain those criteria for relevant services of a telecom operator. For the purpose of identifying services of interest, we first provide short results of user traffic analysis within the telecom operator network. Next, our work presents testbed measurements which explore the impact of packet loss and delay on user QoE for video, voice, and management traffic. For video services, we investigated separately multicast delivery, unicast HTTP Live Streaming (HLS), and unicast Real Time Streaming Protocol (RTSP) traffic. Applying a threshold to QoE values, from the measured dependencies we extracted minimum network performance criteria for the investigated different types of traffic. Finally, we provide a comparison with results available in the literature on the topic

Seamless Mobile Communications for mHealth

There is a growing trend in the health domain to incorporate Smartphones and other wireless technologies to provide more efficient, cost effective, and higher quality healthcare. With newer more sophisticated mobile devices for example, Smartphones this is an escalating practice. To date the use of mobile phone technology in the healthcare domain (mHealth) has been limited to uses such as disseminating information. However, mHealth is beginning to include software and data applications based on mobile devices and technologies. This movement is largely due to the advent of newer technologies associated with Smartphones. Some Smartphones can now be considered to be intelligent sensors with sensing capabilities such as GPS location, proximity and accelerometers. This paper examines the use of such technology in providing seamless mobile communications for mHealth

An Economic Aspect of Device-to-Device Assisted Offloading in Cellular Networks

Traffic offloading via device-to-device (D2D) communications has been proposed to alleviate the traffic burden on base stations (BSs) and to improve the spectral and energy efficiency of cellular networks. The success of D2D communications relies on the willingness of users to share contents. In this paper, we study the economic aspect of traffic offloading via content sharing among multiple devices and propose an incentive framework for D2D assisted offloading. In the proposed incentive framework, the operator improves its overall profit, defined as the network economic efficiency (ECE), by encouraging users to act as D2D transmitters (D2D-Txs) which broadcast their popular contents to nearby users. We analytically characterize D2D assisted offloading in cellular networks for two operating modes: 1) underlay mode and 2) overlay mode. We model the optimization of network ECE as a two-stage Stackelberg game, considering the densities of cellular users and D2D-Tx’s, the operator’s incentives and the popularity of contents. The closedform expressions of network ECE for both underlay and overlay modes of D2D communications are obtained. Numerical results show that the achievable network ECE of the proposed incentive D2D assisted offloading network can be significantly improved with respect to the conventional cellular networks where the D2D communications are disabled

MAROQ: um modelo de alocação de recursos orientado a qualidade de experiência / MAROQ: a quality of experience oriented resource allocation model

A quantidade de recursos fornecidos pelas nuvens computacionais na Internet gerou desafios complexos para resolver problemas relacionados à alocação desses recursos. A qualidade de experiência surge como um paradigma diferenciado como um fator potencialmente importante na solução desses desafios. A qualidade de experiência leva em consideração parametrosˆ de contexto. Sendo assim, é proposto neste trabalho o modelo de alocação de recursos MAROQ, que é um modelo orientado à qualidade de experiência, que utiliza informações de contexto para alocação de recursos em nuvens e grids computacionais. Resultados experimentais mostram que utilizar informações de contexto melhora o desempenho na submissão de tarefas

A proposed framework for mobile Internet QoS customer satisfaction using big data analytics techniques

In the past few years, the Nigeria telecommunication industry has experienced tremendous growth and changes to the extent that customers find it much easier to access the internet through their mobile phones.However, the growth in mobile telecoms subscribers comes with challenges of quality of service, which lead to fluctuations in customer satisfaction.Therefore, the present study proposed a customer satisfaction prediction model through the Key performance indicators obtained from the objective measurement of the network traffic using extended and exhaustive study of the literature.The proposed framework would guide mobile network operators on strategies to embark on in order to retain their customers within the network