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  • 1.
    Alizadeh Noghani, Kyoomars
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ghazzai, Hakim
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    A Generic Framework for Task Offloading in mmWave MEC Backhaul Networks2018In: 2018 IEEE Global Communications Conference (GLOBECOM), 2018, p. 1-7Conference paper (Refereed)
    Abstract [en]

    With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.

  • 2.
    Alsharoa, A.
    et al.
    University of Central Florida.
    Ghazzai, Hakim
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Yuksel, M.
    University of Central Florida.
    Kadri, A.
    Qatar Mobility Innovations Center (QMIC), Qatar University.
    Kamal, A. E.
    Iowa State University (ISU), Ames.
    Trajectory optimization for multiple UAVs acting as wireless relays2018In: 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1-6Conference paper (Refereed)
    Abstract [en]

    This paper proposes a novel wireless relay selection scheme involving multiple mobile Unmanned Aerial Vehicles (UAVs) to support communicating ground users. The goal is to optimize the transmit power levels and trajectories of the relaying UAVs in order to maximize the data rate transmission of the ground users which are suffering from the absence of direct link. Assuming that each UAV is initially characterized by a predefined trajectory for a primary task, we propose to modify it whenever it is needed and the energy and trajectory boundaries constraints allow. We propose to solve this problem using an iterative two steps solution; first, a Mixed Integer Linear Programming (MILP) problem is formulated to optimally determine the users- UAVs associations and the UAVs' corresponding transmit power levels. In the second step, an efficient algorithm based on a recursive shrink-and-realign process is proposed to optimize the UAV trajectories. The performance of the proposed method shows advantages in terms of average throughput compared to the predefined trajectories solution.

  • 3.
    Ghazzai, Hakim
    et al.
    Stevens Institute of Technology, Hoboken, NJ, USA.
    Ghorbel, Madi Ben
    The University of British Columbia, Okanagan Campus, Kelowna, BC, Canada.
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Hossain, Md Jahangir
    The University of British Columbia, Okanagan Campus, Kelowna, BC, Canada.
    Trajectory Optimization for Cooperative Dual-Band UAV Swarms2018In: 2018 IEEE Global Communications Conference (GLOBECOM 2018), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse wireless communication fields. They can act as high- altitude flying relays to support communications between ground nodes due to their ability to provide line-of- sight links. With the flourishing Internet of Things, several types of new applications are emerging. In this paper, we focus on bandwidth hungry and delay-tolerant applications where multiple pairs of transceivers require the support of UAVs to complete their transmissions. To do so, the UAVs have the possibility to employ two different bands namely the typical microwave and the high-rate millimeter wave bands. In this paper, we develop a generic framework to assign UAVs to supported transceivers and optimize their trajectories such that a weighted function of the total service time is minimized. Taking into account both the communication time needed to relay the message and the flying time of the UAVs, a mixed non-linear programming problem aiming at finding the stops at which the UAVs hover to forward the data to the receivers is formulated. An iterative approach is then developed to solve the problem. First, a mixed linear programming problem is optimally solved to determine the path of each available UAV. Then, a hierarchical iterative search is executed to enhance the UAV stops' locations and reduce the service time. The behavior of the UAVs and the benefits of the proposed framework are showcased for selected scenarios.

  • 4.
    Santos, Ricardo
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Ghazzai, Hakim
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). Stevens Institute of Technology, USA.
    Kassler, Andreas
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).
    Optimal Steerable mmWave Mesh Backhaul Reconfiguration2018In: 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Future 5G mobile networks will require increased backhaul (BH) capacity to connect a massive amount of high capacity small cells (SCs) to the network. Because having an optical connection to each SC might be infeasible, mmWave-based (e.g. 60 GHz) BH links are an interesting alternative due to their large available bandwidth. To cope with the increased path loss, mmWave links require directional antennas that should be able to direct their beams to different neighbors, to dynamically change the BH topology, in case new nodes are powered on/off or the traffic demand has changed. Such BH adaptation needs to be orchestrated to minimize the impact on existing traffic.This paper develops a Software-defined networking-based framework that guides the optimal reconfiguration of mesh BH networks composed by mmWave links, where antennas need to be mechanically aligned.By modelling the problem as a Mixed Integer Linear Program (MILP), its solution returns the optimal ordering of events necessary to transition between two BH network configurations. The model creates backup paths whenever it is possible, while minimizing the packet loss of ongoing flows. A numerical evaluation with different topologies and traffic demands shows that increasing the number of BH interfaces per SC from 2 to 4 can decrease the total loss by more than 50%. Moreover, when increasing the total reconfiguration time, additional backup paths can be created, consequently reducing the reconfiguration impact on existing traffic.

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