6,946 research outputs found

    Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

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    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

    Generic Model and Architecture for Cooperating Objects in Sensor Network Environments

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    The complexity and heterogeneity of cooperating object applications in ubiquitous environments or of applications in the sensor network domain require the use of generic models and architectures. These architectures should provide support for the following three key issues: flexible installation, management and reconfiguration of components in the system; optimization strategies whose implementation usually involves the proper management of cross-layer information; and proper adaptation techniques that allow for the self-configuration of nodes and components in the system with minimal human intervention. In this paper, we present one possible instance of such a generic model and architecture and show its applicability using Sustainable Bridges, a sensor network application that requires the analysis of complex sensor data to achieve its goal of effectively monitoring bridges for the detection of structural defects

    Optimize Power Allocation Scheme to Maximize Sum Rate in CoMP with Limited Channel State Information

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    Extensive use of mobile applications throws many challenges in cellular systems like cell edge throughput, inter cell interference and spectral e�ciency. Many of these challenges have been resolved using Coordinated Multi-Point (CoMP), developed in the Third Generation Partnership Project for LTE-Advanced) to a great extent. CoMP cooperatively process signals from base sta- tions that are connected to various multiple terminals (user equipment (UEs)) at transmission and reception. This CoMP improves throughput, reduces or even removes inter-cell interference and increases spectral e�ciency in the downlink of multi-antenna coordinated multipoint systems. Many researchers addressed these issues assuming that BSs have the knowledge of the common control channels dedicated to all UEs and also about the full or partial channel state information (CSI) of all the links. From the CSI available at the BSs, multiuser interference can be managed at the BSs. To make this feasible, UEs are responsible for collecting downlink CSI. But, CSI measurement (instantaneous and/or statistical) is imperfect in nature because of the randomly varying nature of the channels at random times. These incorrect CSI values available at the BSs may, in turn, create multi-user interference. There are many techniques to suppress the multi-user interference, among which the feedback scheme is the one which is gaining a lot of attention. In feedback schemes, CSI information needs to be fed back to the base station from UEs in the uplink. It is obvious, the question arises on the type and amount of feedback need to be used. Research has been progressing in this front and some feedback techniques have been proposed. Three basic CoMP Feedback schemes are available. Explicit or statistical channel information feedback scheme in which channel information like channels's covariance matrix of the channel are shared between the transmitter and receiver. Next, implicit or statistical channel information feedback which contains information such as Channel quality indication or Precoding matrix indicator or Rank indicator. 1st applied to TDD LTE type structure and 2nd of feedback scheme can be applied in the FDD system. Finally, we have UE which tranmit the sounding reference signal (CSI). This type of feedback scheme is applied to exploit channel reciprocity and to reduce channel intercell interference and this can be applied in the TDD system. We have analyzed the scenario of LTE TDD based system. After this, optimization of power is also required because users at the cell edge required more attention than the user locating at the center of the cell. In my work, it shows estimated power gives exponential divercity for high SNR as low SNR too. In this method, a compression feedback method is analyzed to provide multi-cell spatial channel information. It improves the feedback e�ciency and throughput. The rows and columns of the channel matrix are compressed using Eigenmode of the user and codebook based scheme speci�ed in LTE speci�cation. The main drawback of this scheme is that spectral e�ciency is achieved with the cost of increased overheads for feedback and evolved NodeB (eNB). Other factor is complexity of eNodeB which is to be addressed in future work

    Mobile Computing in Digital Ecosystems: Design Issues and Challenges

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    In this paper we argue that the set of wireless, mobile devices (e.g., portable telephones, tablet PCs, GPS navigators, media players) commonly used by human users enables the construction of what we term a digital ecosystem, i.e., an ecosystem constructed out of so-called digital organisms (see below), that can foster the development of novel distributed services. In this context, a human user equipped with his/her own mobile devices, can be though of as a digital organism (DO), a subsystem characterized by a set of peculiar features and resources it can offer to the rest of the ecosystem for use from its peer DOs. The internal organization of the DO must address issues of management of its own resources, including power consumption. Inside the DO and among DOs, peer-to-peer interaction mechanisms can be conveniently deployed to favor resource sharing and data dissemination. Throughout this paper, we show that most of the solutions and technologies needed to construct a digital ecosystem are already available. What is still missing is a framework (i.e., mechanisms, protocols, services) that can support effectively the integration and cooperation of these technologies. In addition, in the following we show that that framework can be implemented as a middleware subsystem that enables novel and ubiquitous forms of computation and communication. Finally, in order to illustrate the effectiveness of our approach, we introduce some experimental results we have obtained from preliminary implementations of (parts of) that subsystem.Comment: Proceedings of the 7th International wireless Communications and Mobile Computing conference (IWCMC-2011), Emergency Management: Communication and Computing Platforms Worksho
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