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Quanxin Zhao

Other affiliations: University of Exeter
Bio: Quanxin Zhao is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Optimization problem & Small cell. The author has an hindex of 7, co-authored 19 publications receiving 703 citations. Previous affiliations of Quanxin Zhao include University of Exeter.

Papers
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Journal ArticleDOI
TL;DR: An optimization problem is formulated to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration, and an EECO scheme is designed, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints.
Abstract: Mobile edge computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading (EECO) mechanisms for MEC in 5G heterogeneous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogeneous network, we then design an EECO scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.

730 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: A novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer, Mobile-Edge Computing and Full-Duplex is provided.
Abstract: Prolonging battery lifetime, enhancing computation capability and improving spectral efficiency have been the key design challenges in Internet of Things (IoT) era. This paper provides a novel solution to jointly optimize the allocation of the communication, computing and energy resources in IoT, with the aid of some advanced wireless communication technologies including Wireless Energy Transfer (WET), Mobile-Edge Computing (MEC) and Full-Duplex (FD). Specifically, the Hybrid Access-Point (HAP) (integrated with a MEC server) operates in FD mode to simultaneously broadcast energy and receive computation tasks to/from the mobile devices in the same band. Each mobile relies on the harvested energy to accomplish computation tasks by locally executing or (partial) offloading to the HAP. We concentrate on max-min energy efficiency optimization problem (MMEP) with the joint the optimization of the transmission power at the HAP, computation energy consumption and offloaded bits at each mobile device, time slots for energy transfer and computation offloading. We study the cases with perfect and imperfect self-interference cancellation at the HAP. To solve the non-convex MMEP, we apply the fractional programming theory and Block Coordinate Descent (BCD) method to design the algorithms with low complexity. Numerical results demonstrate that the proposed solutions outperform the baseline scheme in terms of the worst-case mobile EE. Moreover, the proposed algorithms can converge to the optimal solution through a few iterations.

59 citations

Proceedings ArticleDOI
01 May 2017
TL;DR: Simulation results show that the proposed hierarchical RA algorithm and the two-stage game can achieve a higher EE and a lower computation complexity.
Abstract: This paper focuses on resource allocation in heterogeneous Ultra Dense small-cell Networks (UDNs), in which massive overlaid small cells are under the coverage of a macro cell. In UDN, both co-tier and cross-tier interference need to be taken into account. When increasing the deployment density of Small-cell Base Stations (SBSs) and the unreasonable energy usage, it results in serious interference and degrades the user's satisfaction, which encourages us to agree on maximizing the total system Energy Efficiency (EE). This problem is non-convex and cannot be solved within polynomial time. Therefore, an efficient hierarchical Resource Allocation (RA) algorithm with reduced computational complexity is proposed to decompose the EE optimization problem into two sub-optimization problems: Sub-Channel Allocation (SCA) process and Power Allocation (PA) process. To solve these two processes, thereafter, we employ a uniform pricing scheme to reduce the feedback overhead and build the PA problem into a two-stage Stackelberg game, where the Macro-cell Base Station (MBS) acts as a follower and SBSs are leaders. The obtained simulation results show that the proposed hierarchical RA algorithm and the two-stage game can achieve a higher EE and a lower computation complexity.

31 citations

Proceedings ArticleDOI
01 Dec 2017
TL;DR: This paper forms a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit-cycle frequency, peak transmission power, and energy causality constraints and proposes a joint computation allocation and resource management algorithm.
Abstract: Prolonging battery lifetime and enhancing computation capability have been the key challenges for designing the mobile devices in the Internet of Things (IoT) era. The investigation of Mobile-Edge Computing (MEC) with Wireless Energy Transfer (WET) is a promising solution to overcome such challenges. In this paper, we study the fundamental tradeoff between Energy Efficiency (EE) and delay in the multi-user wireless powered MEC systems. In order to tackle the randomness of channel conditions and task arrivals, we formulate a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit (CPU)-cycle frequency, peak transmission power, and energy causality constraints. Furthermore, we propose a joint computation allocation and resource management algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory, whose convexity is further proved. Specifically, the proposed algorithm with low complexity requires no prior distribution knowledge of channel conditions and task arrivals. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V ),O(V )] and provides a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impacts of various parameters to the system performance.

28 citations

Proceedings ArticleDOI
01 Oct 2015
TL;DR: This paper develops an Overlapping-Reduced Augmented-Reality Multi-view-video (ORARM) architecture for ARM, and proposes a novel network architecture named Hybrid-Unicasting-Multicasting Device-to-device (HUMD), and a low-complexity resource scheduling scheme named Offloading Traffic Scheduling (OTS).
Abstract: It can be very challenging to guarantee video applications with strict Quality-of-Service (QoS) for bandwidth limited wireless small cell networks. Augmented-Reality Multi-view-video (ARM), with the characteristics of 1) virtual video integrated by augmented reality technology and 2) multiple video streams captured by multiple cameras, can not only enrich video experience but provide changeable viewpoints. To eliminate redundant Overlapping Frames (OFs) and common virtual parts, this paper develops an Overlapping-Reduced Augmented-Reality Multi-view-video (ORARM) architecture for ARM. Furthermore, for guaranteeing the required QoS in small cell networks, we propose a novel network architecture named Hybrid-Unicasting-Multicasting Device-to-device (HUMD). After that, with the aim of minimizing small cells bandwidth consumption, we formulate a problem under the HUMD architecture. Then, a low-complexity resource scheduling scheme named Offloading Traffic Scheduling (OTS) is then proposed. The obtained evaluation have demonstrated that OTS can achieve better bandwidth saving performance than that of the conventional solutions.

13 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper describes major use cases and reference scenarios where the mobile edge computing (MEC) is applicable and surveys existing concepts integrating MEC functionalities to the mobile networks and discusses current advancement in standardization of the MEC.
Abstract: Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

1,829 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a survey of the research on computation offloading in mobile edge computing (MEC), focusing on user-oriented use cases and reference scenarios where the MEC is applicable.
Abstract: Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

1,759 citations

Journal ArticleDOI
TL;DR: A comprehensive survey, analyzing how edge computing improves the performance of IoT networks and considers security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and proposing a framework for security evaluation of IoT Networks with edge computing.
Abstract: The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

1,008 citations

Journal ArticleDOI
TL;DR: This survey makes an exhaustive review on the state-of-the-art research efforts on mobile edge networks, including definition, architecture, and advantages, and presents a comprehensive survey of issues on computing, caching, and communication techniques at the network edge.
Abstract: As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

782 citations

Journal ArticleDOI
TL;DR: This paper is the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the Integration of all the three network segments.
Abstract: Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks, and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics, such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management, and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

661 citations