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  • 1.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Du, Rong
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Computation Rate Maximization for Wireless Powered Mobile Edge Computing with NOMA2019In: Proceedings 20th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM 2019), IEEE , 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider a mobile edge computing (MEC) network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC as a whole. For the offloading users, non-orthogonal multiple access (NOMA) is adopted for information transmission. We consider rate-adaptive computational tasks and aim at maximizing the sum computation rate of all users by jointly optimizing the individual computing mode selection (local computing or offloading), the time allocations for energy transfer and for information transmission, together with the local computing speed or the transmission power level. The major difficulty of the rate maximization problem lies in the combinatorial nature of the multiuser computing mode selection and its involved coupling with the time allocation. We also study the case where the offloading users adopt time division multiple access (TDMA) as a benchmark, and derive the optimal time sharing among the users. We show that the maximum achievable rate is the same for the TDMA and the NOMA system, and in the case of NOMA it is independent from the decoding order, which can be exploited to improve system fairness. To maximize the sum computation rate, for the mode selection we propose a greedy solution based on the wireless channel gains, combined with the optimal allocation of energy transfer time. Numerical results show that the proposed solution maximizes the computation rate in homogeneous networks, and binary offloading leads to significant gains. Moreover, NOMA increases the fairness of rate distribution among the users significantly, when compared with TDMA.

  • 2.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Fodor, Viktoria
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Distributing Dynamic Divisible Loads2017In: 2017 IFIP Networking Conference (IFIP Networking) and Workshops, Institute of Electrical and Electronics Engineers (IEEE), 2017Conference paper (Refereed)
    Abstract [en]

    With the emergence of computing infrastructures in the cloud or at the network edge we need to address the question of how to utilize these shared resources when computational tasks are generated dynamically. While small computing tasks may be satisfied with the computing capacity of a single resource, large tasks may want to utilize multiple computing nodes and perform parallel processing to shorten the task completion time. In this paper we evaluate how additional overhead in such divisible load systems affect the efficiency of parallel processing - from the point of view of the task itself, and for the entire resource sharing system. We show that the preference of a single task may be in conflict with the allocation needed for a social optimum, which in turn depends heavily on the load as well as on the system size.

  • 3.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering. KTH Royal Inst Technol, Sch Elect Engn & Comp Sci, S-10044 Stockholm, Sweden..
    Dynamic Spectrum Sharing for Load Balancing in Multi-Cell Mobile Edge Computing2020In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 9, no 2, p. 189-193Article in journal (Refereed)
    Abstract [en]

    Large-scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations (BSs), together with local resource allocation in the cells. We show that the network-wide resource allocation can be transformed into a convex optimization problem, and propose a distributed, hierarchical solution with limited information exchange among the BSs. Numerical results demonstrate that the proposed solution is superior to other baseline algorithms, when wireless and computing resource allocation is not jointly optimized, or the wireless resources allocated to the BSs are fixed.

  • 4.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Energy minimization for delay constrained mobile edge computing with orthogonal and non-orthogonal multiple access2020In: Ad hoc networks, ISSN 1570-8705, E-ISSN 1570-8713, Vol. 98, article id 102060Article in journal (Refereed)
    Abstract [en]

    Mobile edge computing (MEC) is envisioned as a promising technology for enhancing the computation capacities and prolonging the lifespan of mobile devices, by enabling mobile devices to offload computation-intensive tasks to servers in close proximity. For wireless communication, MEC introduces a new scenario, where computations are performed directly at the receiving side of the wireless links. Our objective is therefore to evaluate the importance of joint radio-and-computational resource allocation and spectral efficiency enhancing techniques in this new scenario. We formulate the resource allocation problem to minimize the energy consumption of computation offloading of delay sensitive tasks and propose near-optimal solutions for both orthogonal and non-orthogonal multiple access schemes, with the optimal joint allocation of computing resources and transmission power. Our numerical results demonstrate the superiority of non-orthogonal multiple access over its orthogonal counterpart and the importance of joint resource allocation, especially in scenarios with strict delay limits, where both the transmission and the computational resources are scarce.

  • 5.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Energy-efficient Resource Allocation for NOMA-assisted Mobile Edge Computing2018In: 2018 IEEE 29TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC), IEEE , 2018, p. 1794-1799Conference paper (Refereed)
    Abstract [en]

    In this paper we evaluate the effect of increased wireless spectral efficiency on the performance of mobile edge computing. Specifically, we study the energy minimization of computation offloading for a multi carrier non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system. A joint radio-and-computational resource allocation problem is formulated, in which three different resources should be appropriately allocated, including subcarriers, transmission power and computational resources. The formulated resource allocation problem belongs to mixed integer nonlinear programming (MILNP) and is NP-hard. We propose therefore a heuristic solution consisting of two steps, NOMA clustering and subcarrier allocation, and joint computational resource and power allocation. Our numerical results show that NOMA based MEC significantly outperforms its OMA counterpart, especially in scenarios with strict delay limits, where both the transmission and the computational resources become scarce.

  • 6.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    On the Performance of Parallel Processing in Dynamic Resource Sharing Systems2019In: Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 30-36Conference paper (Refereed)
    Abstract [en]

    Parallel processing has the potential of significantly decreasing the service time for a single computational task. Meanwhile, as each task occupies more resources, the number of simultaneously supported tasks declines. This tradeoff is interesting when resources are accessed by many users in a dynamic way, like in the case of cloud or fog computing. In this paper, we evaluate how the level of parallelization and the eventual overheads affect the response time in these dynamic resource sharing systems. We show the counterintuitive finding that even when parallelization has no overhead, the allocation of all resources to a task is suboptimal if the service times have large coefficient of variation. Moreover, we evaluate the scalability properties, and provide guidelines for the optimal level of parallelization under different types of overhead.

  • 7.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Sum-Rate Maximization under QoS Constraint in MIMO-NOMA Systems2018In: 2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), IEEE , 2018Conference paper (Refereed)
    Abstract [en]

    This paper addresses the power allocation challenge for the downlink transmission in non-orthogonal multiple access (NOMA) systems applying multiple input multiple output transceivers. We consider the case when users are paired to form NOMA clusters, and share a common power budget. We provide low complexity power allocation methods within the clusters and across the clusters, that, together, maximize the sum-rate of the network, while guaranteeing a minimum quality of service for the users with weak channel condition. We show that compared to equal power allocation for the clusters, the proposed power allocation scheme improves the system fairness significantly, without decreasing the aggregate performance.

1 - 7 of 7
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  • de-DE
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  • fi-FI
  • nn-NO
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