Device-to-device communications for enhancing quality of experience in software defined multi-tier LTE-A networks

Li, Jiajia; Zhang, Shangwei; Kato, Nei; Ujikawa, Hirotaka; Suzuki, Keisuke

doi:10.1109/mnet.2015.7166190

Cited by 183 publications

(50 citation statements)

References 10 publications

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“…Mostly sensor node topology is a mesh topology or a peer-topeer topology; management and control in constrained environment are always a vigorous research area. In this context of mesh network connectivity, an interesting instigating architecture for a wireless mesh network (WMN) on the basis of OpenFlow was given by Delay et al in [23]. In this paper, the author suggests the seamless mobility in the WMN by the use of OpenFlow.…”

Section: Sdn For Wireless Sensor Based Iot Devicesmentioning

confidence: 99%

Software-Defined Networks (SDNs) and Internet of Things (IoTs): A Qualitative Prediction for 2020

Tayyaba¹,

Shah²,

Khan³

et al. 2016

ijacsa

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Section: Sdn For Wireless Sensor Based Iot Devicesmentioning

confidence: 99%

Software-Defined Networks (SDNs) and Internet of Things (IoTs): A Qualitative Prediction for 2020

Tayyaba¹,

Shah²,

Khan³

et al. 2016

ijacsa

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“…Hence, resource allocation becomes the key issue in D2D communication, and appropriate resource allocation schemes are imperative to be conducted to settle this issue [9]. Considering the fact that the D2D communication shares the uplink spectrum resource, Xu et al utilize a reverse iterative combinatorial auction based mechanism to accomplish the resource allocation [10].…”

Section: Introductionmentioning

confidence: 99%

Game‐Theoretic Social‐Aware Resource Allocation for Device‐to‐Device Communications Underlaying Cellular Network

Wang

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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Device-to-Device communication underlaying cellular network can increase the spectrum efficiency due to direct proximity communication and frequency reuse. However, such performance improvement is influenced by the power interference caused by spectrum sharing and social characteristics in each social community jointly. In this investigation, we present a dynamic game theory with complete information based D2D resource allocation scheme for D2D communication underlaying cellular network. In this resource allocation method, we quantify both the rate influence from the power interference caused by the D2D transmitter to cellular users and rate enhancement brought by the social relationships between mobile users. Then, the utility function maximization game is formulated to optimize the overall transmission rate performance of the network, which synthetically measures the final influence from both power interference and sociality enhancement. Simultaneously, we discuss the Nash Equilibrium of the proposed utility function maximization game from a theoretical point of view and further put forward a utility priority searching algorithm based resource allocation scheme. Simulation results show that our proposed scheme attains better performance compared with the other two advanced proposals.

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“…The cloud radio access network (CRAN), as an example on the radio access network (RAN) side to achieve SoftRAN, decouples baseband processing from radio frequency (RF) transmission of remote radio heads (RRHs) and centralizes it in a cloud-based pool of baseband units (BBUs) [10]. In [11], the authors provided a device-to-device communication based algorithm in software defined multi-tier long term evolution (LTE) networks. The research on fog computing is in its infancy stage with most efforts to specify definitions, architecture, and user cases.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Architecture for Software Defined and Virtualized Radio Access Networks with Fog Computing

et al. 2017

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© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. Takedown AbstractToday, billions of communication devices connecting to wireless networks impose serious challenges in network deployment, management, and data processing. Amongst all emerging technologies tackling these challenges, software defined networks (SDNs) decouple control plane from data plane to provide network programmability and virtualization, which can share network and radio resources among various applications. On the other hand, fog computing offloads computing services from cloud to the edge of networks, offering real time data services to nearby data terminals. In this paper, we present an integrated architecture for software defined and virtualized radio access networks with fog computing. We propose a design of software-as-a-service (SaaS) called OpenPipe, which enables network level virtualizaiton. To integrate SDNs and network virtualization with fog computing, we adopt a hybrid control model with two hierarchical control levels, where a SDN controller forms the higher level, and local controllers comprise the lower level. Typical user cases of the proposed network architecture are validated through laboratory demonstrations.

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Device-to-device communications for enhancing quality of experience in software defined multi-tier LTE-A networks

Cited by 183 publications

References 10 publications

Software-Defined Networks (SDNs) and Internet of Things (IoTs): A Qualitative Prediction for 2020

Software-Defined Networks (SDNs) and Internet of Things (IoTs): A Qualitative Prediction for 2020

Game‐Theoretic Social‐Aware Resource Allocation for Device‐to‐Device Communications Underlaying Cellular Network

An Integrated Architecture for Software Defined and Virtualized Radio Access Networks with Fog Computing

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