2 research outputs found
Optimal Resource Allocation for Wireless Powered Mobile Edge Computing with Dynamic Task Arrivals
This paper considers a wireless powered multiuser mobile edge computing (MEC)
system, where a multi-antenna access point (AP) employs the radio-frequency
(RF) signal based wireless power transfer (WPT) to charge a number of
distributed users, and each user utilizes the harvested energy to execute
computation tasks via local computing and task offloading. We consider the
frequency division multiple access (FDMA) protocol to support simultaneous task
offloading from multiple users to the AP. Different from previous works that
considered one-shot optimization with static task models, we study the joint
computation and wireless resource allocation optimization with dynamic task
arrivals over a finite time horizon consisting of multiple slots. Under this
setup, our objective is to minimize the system energy consumption including the
AP's transmission energy and the MEC server's computing energy over the whole
horizon, by jointly optimizing the transmit energy beamforming at the AP, and
the local computing and task offloading strategies at the users over different
time slots. To characterize the fundamental performance limit of such systems,
we focus on the offline optimization by assuming the task and channel
information are known a-priori at the AP. In this case, the energy minimization
problem corresponds to a convex optimization problem. Leveraging the Lagrange
duality method, we obtain the optimal solution to this problem in a well
structure. It is shown that in order to maximize the system energy efficiency,
the optimal number of task input-bits at each user and the AP are monotonically
increasing over time, and the offloading strategies at different users depend
on both the wireless channel conditions and the task load at the AP. Numerical
results demonstrate the benefit of the proposed joint-WPT-MEC design over
alternative benchmark schemes without such joint design.Comment: 7 pages, 3 figures, and Accepted by IEEE ICC 2019, Shanghai, Chin
Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems
This paper studies a wireless powered mobile edge computing (MEC) system with
fluctuating channels and dynamic task arrivals over time. We jointly optimize
the transmission energy allocation at the energy transmitter (ET) for WPT and
the task allocation at the user for local computing and offloading over a
particular finite horizon, with the objective of minimizing the total
transmission energy consumption at the ET while ensuring the user's successful
task execution. First, in order to characterize the fundamental performance
limit, we consider the offline optimization by assuming that the perfect
knowledge of channel state information and task state information (i.e., task
arrival timing and amounts) is known a-priori. In this case, we obtain the
well-structured optimal solution in a closed form to the energy minimization
problem via convex optimization techniques. Next, inspired by the structured
offline solutions obtained above, we develop heuristic online designs for the
joint energy and task allocation when the knowledge of CSI/TSI is only causally
known. Finally, numerical results are provided to show that the proposed joint
designs achieve significantly smaller energy consumption than benchmark schemes
with only local computing or full offloading at the user, and the proposed
heuristic online designs perform close to the optimal offline solutions.Comment: One-column 32 pages, 6 figures, and accepted for publication in IEEE
Transactions on Wireless Communication