501 research outputs found
Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading
In this paper, we consider a multi-user mobile edge computing (MEC) network
powered by wireless power transfer (WPT), where each energy-harvesting WD
follows a binary computation offloading policy, i.e., data set of a task has to
be executed as a whole either locally or remotely at the MEC server via task
offloading. In particular, we are interested in maximizing the (weighted) sum
computation rate of all the WDs in the network by jointly optimizing the
individual computing mode selection (i.e., local computing or offloading) and
the system transmission time allocation (on WPT and task offloading). The major
difficulty lies in the combinatorial nature of multi-user computing mode
selection and its strong coupling with transmission time allocation. To tackle
this problem, we first consider a decoupled optimization, where we assume that
the mode selection is given and propose a simple bi-section search algorithm to
obtain the conditional optimal time allocation. On top of that, a coordinate
descent method is devised to optimize the mode selection. The method is simple
in implementation but may suffer from high computational complexity in a
large-size network. To address this problem, we further propose a joint
optimization method based on the ADMM (alternating direction method of
multipliers) decomposition technique, which enjoys much slower increase of
computational complexity as the networks size increases. Extensive simulations
show that both the proposed methods can efficiently achieve near-optimal
performance under various network setups, and significantly outperform the
other representative benchmark methods considered.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems
Mobile edge computing (MEC) and wireless power transfer (WPT) are two
promising techniques to enhance the computation capability and to prolong the
operational time of low-power wireless devices that are ubiquitous in Internet
of Things. However, the computation performance and the harvested energy are
significantly impacted by the severe propagation loss. In order to address this
issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless powered system is
studied in this paper. The computation rate maximization problems in a
UAV-enabled MEC wireless powered system are investigated under both partial and
binary computation offloading modes, subject to the energy harvesting causal
constraint and the UAV's speed constraint. These problems are non-convex and
challenging to solve. A two-stage algorithm and a three-stage alternative
algorithm are respectively proposed for solving the formulated problems. The
closed-form expressions for the optimal central processing unit frequencies,
user offloading time, and user transmit power are derived. The optimal
selection scheme on whether users choose to locally compute or offload
computation tasks is proposed for the binary computation offloading mode.
Simulation results show that our proposed resource allocation schemes
outperforms other benchmark schemes. The results also demonstrate that the
proposed schemes converge fast and have low computational complexity.Comment: This paper has been accepted by IEEE JSA
Multiuser Computation Offloading and Downloading for Edge Computing with Virtualization
Mobile-edge computing (MEC) is an emerging technology for enhancing the
computational capabilities of mobile devices and reducing their energy
consumption via offloading complex computation tasks to the nearby servers.
Multiuser MEC at servers is widely realized via parallel computing based on
virtualization. Due to finite shared I/O resources, interference between
virtual machines (VMs), called I/O interference, degrades the computation
performance. In this paper, we study the problem of joint radio-and-computation
resource allocation (RCRA) in multiuser MEC systems in the presence of I/O
interference. Specifically, offloading scheduling algorithms are designed
targeting two system performance metrics: sum offloading throughput
maximization and sum mobile energy consumption minimization. Their designs are
formulated as non-convex mixed-integer programming problems, which account for
latency due to offloading, result downloading and parallel computing. A set of
low-complexity algorithms are designed based on a decomposition approach and
leveraging classic techniques from combinatorial optimization. The resultant
algorithms jointly schedule offloading users, control their offloading sizes,
and divide time for communication (offloading and downloading) and computation.
They are either optimal or can achieve close-to-optimality as shown by
simulation. Comprehensive simulation results demonstrate considering of I/O
interference can endow on an offloading controller robustness against the
performance-degradation factor
Wireless Powered User Cooperative Computation in Mobile Edge Computing Systems
This paper studies a wireless powered mobile edge computing (MEC) system,
where a dedicated energy transmitter (ET) uses the radio-frequency (RF) signal
enabled wireless power transfer (WPT) to charge wireless devices for
sustainable computation. In such a system, we present a new user cooperation
approach to improve the computation performance of active devices, in which
surrounding idle devices are enabled as helpers to use their opportunistically
harvested wireless energy from the ET to help remotely execute active users'
computation tasks. In particular, we consider a basic scenario with one user
(with computation tasks to execute) and multiple helpers, in which the user can
partition the computation tasks into various parts for local execution and
computation offloading to helpers, respectively. Both the user and helpers are
subject to the so-called energy neutrality constraints, such that their energy
consumption does not exceed the respective energy harvested from the ET. Under
this setup and considering a frequency division multiple access (FDMA) based
computation offloading protocol, we maximize the computation rate (i.e., the
number of computation bits over a particular time block) of the user, by
jointly optimizing the transmit energy beamforming at the ET, as well as the
communication and computation resource allocations at both the user and
helpers. By leveraging the Lagrange duality method, we present the optimal
solution to this problem in a semi-closed form. Numerical results show that the
proposed wireless powered user cooperative computation design significantly
improves the computation rate at the user, as compared to conventional schemes
without such cooperation.Comment: 8 pages, 5 figures, accepted by Proc. IEEE GLOBECOM 2018 Workshop
Computation Efficiency Maximization in Wireless-Powered Mobile Edge Computing Networks
Energy-efficient computation is an inevitable trend for mobile edge computing
(MEC) networks. Resource allocation strategies for maximizing the computation
efficiency are critically important. In this paper, computation efficiency
maximization problems are formulated in wireless-powered MEC networks under
both partial and binary computation offloading modes. A practical non-linear
energy harvesting model is considered. Both time division multiple access
(TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated
for offloading. The energy harvesting time, the local computing frequency, and
the offloading time and power are jointly optimized to maximize the computation
efficiency under the max-min fairness criterion. Two iterative algorithms and
two alternative optimization algorithms are respectively proposed to address
the non-convex problems formulated in this paper. Simulation results show that
the proposed resource allocation schemes outperform the benchmark schemes in
terms of user fairness. Moreover, a tradeoff is elucidated between the
achievable computation efficiency and the total number of computed bits.
Furthermore, simulation results demonstrate that the partial computation
offloading mode outperforms the binary computation offloading mode and NOMA
outperforms TDMA in terms of computation efficiency.Comment: This paper has been accepted for publication in IEEE Transactions on
Wireless Communication
Computation Rate Maximization for Wireless Powered Mobile Edge Computing
Integrating mobile edge computing (MEC) and wireless power transfer (WPT) has
been regarded as a promising technique to improve computation capabilities for
self-sustainable Internet of Things (IoT) devices. This paper investigates a
wireless powered multiuser MEC system, where a multi-antenna access point (AP)
(integrated with an MEC server) broadcasts wireless power to charge multiple
users for mobile computing. We consider a time-division multiple access (TDMA)
protocol for multiuser computation offloading. Under this setup, we aim to
maximize the weighted sum of the computation rates (in terms of the number of
computation bits) across all the users, by jointly optimizing the energy
transmit beamformer at the AP, the task partition for the users (for local
computing and offloading, respectively), and the time allocation among the
users. We derive the optimal solution in a semi-closed form via convex
optimization techniques. Numerical results show the merit of the proposed
design over alternative benchmark schemes.Comment: 6 pages and 2 figure
UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design
With the emergence of diverse mobile applications (such as augmented
reality), the quality of experience of mobile users is greatly limited by their
computation capacity and finite battery lifetime. Mobile edge computing (MEC)
and wireless power transfer are promising to address this issue. However, these
two techniques are susceptible to propagation delay and loss. Motivated by the
chance of short-distance line-of-sight achieved by leveraging unmanned aerial
vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is
studied. A power minimization problem is formulated subject to the constraints
on the number of the computation bits and energy harvesting causality. The
problem is non-convex and challenging to tackle. An alternative optimization
algorithm is proposed based on sequential convex optimization. Simulation
results show that our proposed design is superior to other benchmark schemes
and the proposed algorithm is efficient in terms of the convergence.Comment: This paper has been accepted by IEEE ICC 201
Computation Efficiency Maximization in OFDMA-Based Mobile Edge Computing Networks
Computation-efficient resource allocation strategies are of crucial
importance in mobile edge computing networks. However, few works have focused
on this issue. In this letter, weighted sum computation efficiency (CE)
maximization problems are formulated in a mobile edge computing (MEC) network
with orthogonal frequency division multiple access (OFDMA). Both partial
offloading mode and binary offloading mode are considered. The closed-form
expressions for the optimal subchannel and power allocation schemes are
derived. In order to address the intractable non-convex weighted sum-of ratio
problems, an efficiently iterative algorithm is proposed. Simulation results
demonstrate that the CE achieved by our proposed resource allocation scheme is
better than that obtained by the benchmark schemes.Comment: This paper has been accepted by IEEE Communications Letter
Decentralized Computation Offloading and Resource Allocation in Heterogeneous Networks with Mobile Edge Computing
We consider a heterogeneous network with mobile edge computing, where a user
can offload its computation to one among multiple servers. In particular, we
minimize the system-wide computation overhead by jointly optimizing the
individual computation decisions, transmit power of the users, and computation
resource at the servers. The crux of the problem lies in the combinatorial
nature of multi-user offloading decisions, the complexity of the optimization
objective, and the existence of inter-cell interference. Then, we decompose the
underlying problem into two subproblems: i) the offloading decision, which
includes two phases of user association and subchannel assignment, and ii)
joint resource allocation, which can be further decomposed into the problems of
transmit power and computation resource allocation. To enable distributed
computation offloading, we sequentially apply a many-to-one matching game for
user association and a one-to-one matching game for subchannel assignment.
Moreover, the transmit power of offloading users is found using a bisection
method with approximate inter-cell interference, and the computation resources
allocated to offloading users is achieved via the duality approach. The
proposed algorithm is shown to converge and is stable. Finally, we provide
simulations to validate the performance of the proposed algorithm as well as
comparisons with the existing frameworks.Comment: Submitted to IEEE Journa
Resource Allocation in Full-Duplex Mobile-Edge Computing Systems with NOMA and Energy Harvesting
This paper considers a full-duplex (FD) mobile-edge computing (MEC) system
with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where
one group of users simultaneously offload task data to the base station (BS)
via NOMA and the BS simultaneously receive data and broadcast energy to other
group of users with FD. We aim at minimizing the total energy consumption of
the system via power control, time scheduling and computation capacity
allocation. To solve this nonconvex problem, we first transform it into an
equivalent problem with less variables. The equivalent problem is shown to be
convex in each vector with the other two vectors fixed, which allows us to
design an iterative algorithm with low complexity. Simulation results show that
the proposed algorithm achieves better performance than the conventional
methods
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