An Efficient Resource Allocation Mechanism for LTE–GEPON Converged Networks

Ranaweera, Chathurika; Wong, Elaine; Lim, Christina; Nirmalathas, Ampalavanapillai; Jayasundara, Chamil

doi:10.1007/s10922-013-9283-3

Cited by 12 publications

(5 citation statements)

References 22 publications

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“…This mechanism leverages the characteristics of LTE frame structures and the resource allocation information from the LTE network. While the complete study on this mechanism is reported in [22], here we briefly describe how this forecasting method can be used in conjunction with the proposed ADERA algorithm, in LTE-GEPON converged networks.…”

Section: E Traffic Forecasting Mechanismmentioning

confidence: 99%

Architecture Discovery Enabled Resource Allocation Mechanism for Next Generation Optical-Wireless Converged Networks

Ranaweera

Wong

Lim

et al. 2013

J. Opt. Commun. Netw.

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Optical-wireless convergence is identified as a promising solution to facilitate quality-of-service (QoS)-guaranteed, ubiquitous, and high-bandwidth access to end users. Different converged network architectures can be deployed depending on individual circumstances to achieve improved performance without compromising cost-effectiveness. However, with different network architectures, different resource allocation mechanisms are required to achieve the best performance. This is problematic in both the deployment and operational phases. In this paper, we propose an architecture discovery enabled resource allocation (ADERA) mechanism for the long term evolution (LTE)-gigabit Ethernet passive optical network (GEPON) converged network. The proposed ADERA is a self-adaptive algorithm-it discovers the underlying architecture of the network by analyzing control signals and eventually evolves into an effective resource handling mechanism for the respective architecture. In addition, ADERA leverages inherited features of both the LTE network and GEPON in conjunction with the characteristics of their frame structures to improve the overall network performance. For example, ADERA is incorporated with a near-future traffic forecasting mechanism for efficient resource allocation. Using simulations, we evaluate the performance of our proposed ADERA algorithm and compare it against other existing resource allocation mechanisms. Our results indicate that ADERA achieves improved QoS performance in the converged network irrespective of the architecture used for the deployment.

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Section: E Traffic Forecasting Mechanismmentioning

confidence: 99%

Architecture Discovery Enabled Resource Allocation Mechanism for Next Generation Optical-Wireless Converged Networks

Ranaweera

Wong

Lim

et al. 2013

J. Opt. Commun. Netw.

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“…These approaches in conjunction with the implementation of advanced techniques like MIMO-OFDM, CoMP, dynamic Carrier Aggregation, etc. introduces an exponential increase in the backhaul capacity requirements [9][10][11][12][13]. The major issues that degrade the performance of high speed analogue RoF and digital RoF links are noise, fiber dispersion and nonlinear effects [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An overview of fiber dispersion and nonlinearity compensation techniques in optical orthogonal frequency division multiplexing systems

Ilavarasan

Meenakshi

2015

J Opt

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Optical fiber based transmission network is the key technology to support high capacity backhaul needs for future wireless communication standards. Orthogonal Frequency Division Multiplexing (OFDM), Multiple Input Multiple Output (MIMO) transreception, Carrier Aggregation (CA), Co-operative Multi-Point (Co MP) and Wavelength Division Multiplexing (WDM) for backhaul/backbone are all, state of the art techniques used in most of these standards. The successful implementation of all these technologies requires modification of the network architecture which leads to challenges on backhaul design in terms of capacity and latency requirements. The optical fiber networks implemented in the form of analogue Radio over Fiber (RoF) or digital RoF offers a prospective solution. The performance of analogue RoF suffers from noise and linearity issues and digital RoF is degraded by fiber dispersion and nonlinearity due to high rate of transmission. Dispersion and nonlinearity compensation becomes essential to make the optical fiber backhaul supportive of the emerging wireless technologies. This paper reviews and compares various techniques proposed in the literature for compensating fiber dispersion and nonlinearity. A comprehensive comparison of fiber dispersion and nonlinear effects are summarized. Further, the selection criteria for choosing a particular compensating technique in Optical OFDM and WDM systems have been presented in this work.T. Ilavarasan

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“…The introduction of 4th Generation (4G) technologies promises to meet these expectations by offering high capacity, low latency, and seamless mobility . Toward this direction, the convergence of the wireless access network with a passive optical network at the backhaul has been proposed in order to avoid the creation of bottlenecks and to facilitate the QoS provisioning . However, such an approach raises new issues concerning the resource management of the backhaul network.…”

Section: Introductionmentioning

confidence: 99%

On the predictability of next generation mobile network traffic using artificial neural networks

Loumiotis

Adamopoulou

Demestichas

et al. 2013

Int J Communication

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SUMMARYThough the introduction of the new 4th Generation mobile access technologies promises to satisfy the increasing bandwidth demand of the end-users, it poses in parallel the need for novel resource management approaches at the side of the base station. To this end, schemes that try to predict the forthcoming bandwidth demand using supervised learning methods have been proposed in the literature. However, there are still open issues concerning the training phase of such methods. In the current work, the authors propose a novel scheme that dynamically selects a proper training set for artificial neural network prediction models, based on the statistical characteristics of the collected data. It is demonstrated that an initial statistical processing of the collected data and the subsequent selection of the training set can efficiently improve the performance of the prediction model. Finally, the proposed scheme is validated using network traffic collected by real, fully operational base stations.

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An Efficient Resource Allocation Mechanism for LTE–GEPON Converged Networks

Cited by 12 publications

References 22 publications

Architecture Discovery Enabled Resource Allocation Mechanism for Next Generation Optical-Wireless Converged Networks

Architecture Discovery Enabled Resource Allocation Mechanism for Next Generation Optical-Wireless Converged Networks

An overview of fiber dispersion and nonlinearity compensation techniques in optical orthogonal frequency division multiplexing systems

On the predictability of next generation mobile network traffic using artificial neural networks

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