Fundamentals of Heterogeneous Backhaul Design—Analysis and Optimization

Zhang, Gongzheng; Quek, Tony Q. S.; Kountouris, Marios; Huang, Aiping; Shan, Hangguan

doi:10.1109/tcomm.2016.2515596

Cited by 127 publications

(74 citation statements)

References 34 publications

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“…Currently, two technologies have been proposed for the out-band wireless backhaul, i.e., millimeter wave (mmWave) [16] and sub-6 GHz wireless backhaul. mmWave offers high capacity and reliability based on lineof-sight communications between an MBS and an SC, and sub-6 GHz wireless backhaul provides high data rate based on non-line-of-sight communications between an MBS and an SC [17].…”

Section: Wireless Backhaul Solutionsmentioning

confidence: 99%

Mobile Edge Computing Empowers Internet of Things

Ansari

Sun

2018

IEICE Trans. Commun.

100

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SUMMARY In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in realtime. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

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Section: Wireless Backhaul Solutionsmentioning

confidence: 99%

Mobile Edge Computing Empowers Internet of Things

Ansari

Sun

2018

IEICE Trans. Commun.

100

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“…While being transmitted over wired backhaul link, user data packets are processed at each transmission node, resulting in processing delay. Referring to [21], it can be shown that the average processing delay at the lth hop between GAP i and the gateway when transmitting file k for UE j can be expressed as…”

Section: Backhaul Transmission Delaymentioning

confidence: 99%

Network utility optimization-based joint user association and content placement in heterogeneous networks

Chen

Chai

et al. 2018

J Wireless Com Network

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The rapid growth of traffic demands has posed challenges and difficulties on both the radio access networks (RANs) and the backhaul links. While heterogeneous networks (HetNets) are expected to offer diverse radio access capabilities and improve the transmission performance of user equipments (UEs) significantly through integrating various RANs efficiently, the backhaul links may still experience challenges in offering quality of service (QoS) guaranteed services to UEs. To tackle these problems, caching technology, more specifically, caching user contents at the infrastructures of different RANs is proposed as an effective approach. In this paper, we consider the joint user association and cache content placement problem in cache-enabled HetNets. Stressing the tradeoff between user download delay and caching cost, we introduce the concept of utility function which characterizes the joint network performance as the weighted sum of user download delay and the caching cost and formulate the joint user association and cache content placement problem as a network utility optimization problem. As the formulated optimization problem is a nonlinear integer optimization problem which cannot be solved conveniently using traditional optimization tools, we transform the original optimization problem equivalently into three convex subproblems by applying Lagrange partial relaxation and McCormick envelopes, and then propose an iterative algorithm. Within each iteration, for a given set of Lagrange multipliers, the three subproblems are solved respectively by means of the modified Kuhn-Munkres (K-M) algorithm and the locally optimal solutions can be obtained, based on which the Lagrange multipliers can be updated through applying subgradient method. Simulation results demonstrate the effectiveness of the proposed algorithm.

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“…Q-learning is used to dynamically adjust the CREO of each cell with the aim of maximising the system throughput while avoiding users' assignement to cells with congested backhaul links leading to 15% improvement in users' QoE. In another work, [8], CRE feature is used to reflect the backhaul delay of the corresponding cell and influence users' selection accordingly. Similarly, a joint radio-backhaul delay objective is targeted in [9] in which authors consider a twotier HetNet with wireless backhaul (out-of-band).…”

Section: Backhaul-aware Cell Range Extensionmentioning

confidence: 99%

A Multiple Attribute User-Centric Backhaul Provisioning Scheme Using Distributed SON

Jaber

Imran²,

Tafazolli

et al. 2016

2016 IEEE Global Communications Conference (GLOBECOM)

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Abstract-The backhaul network is a critical challenge towards the success of 5G and corresponding difficulties are many-fold, such as network coverage expansion, very high bandwidth, ultralow latency and energy consumption, at a minimum cost. No single backhaul solution can address all these requirements but, on the other hand, not all of the backhaul links require the same set of stringent requirements. To this end, we propose a novel scheme that capitalises on the diversity in both performance requirements and backhaul capabilities to maximise the systemcentric as well as user-centric performance indicators. The usercentric backhaul provisioning scheme uses multiple attribute decision making (MADM) for the user-cell-backhaul association criteria in a way that intelligently associates users with available cells based on corresponding dynamic radio and backhaul conditions while abiding by users requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator of the endto-end network performance such as capacity, latency, resilience, energy consumption, etc. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul capabilities that conform to the user requirements. Reinforcement learning is used by the radio cell to optimise the bias factors for each performance indicator in a way that maximises the system performance and users end-to-end quality of experience (QoE). Preliminary results based on a case study show considerable improvement in users QoE when compared to state-of-the-art user-cell association schemes.

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Fundamentals of Heterogeneous Backhaul Design—Analysis and Optimization

Cited by 127 publications

References 34 publications

Mobile Edge Computing Empowers Internet of Things

Mobile Edge Computing Empowers Internet of Things

Network utility optimization-based joint user association and content placement in heterogeneous networks

A Multiple Attribute User-Centric Backhaul Provisioning Scheme Using Distributed SON

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