English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 ofdistributed users, and each user utilizes the harvested energy to executecomputation tasks via local computing and task offloading. We consider thefrequency division multiple access (FDMA) protocol to support simultaneous taskoffloading from multiple users to the AP. Different from previous works thatconsidered one-shot optimization with static task models, we study the jointcomputation and wireless resource allocation optimization with dynamic taskarrivals over a finite time horizon consisting of multiple slots. Under thissetup, our objective is to minimize the system energy consumption including theAP's transmission energy and the MEC server's computing energy over the wholehorizon, by jointly optimizing the transmit energy beamforming at the AP, andthe local computing and task offloading strategies at the users over differenttime slots. To characterize the fundamental performance limit of such systems,we focus on the offline optimization by assuming the task and channelinformation are known a-priori at the AP. In this case, the energy minimizationproblem corresponds to a convex optimization problem. Leveraging the Lagrangeduality method, we obtain the optimal solution to this problem in a wellstructure. 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 monotonicallyincreasing over time, and the offloading strategies at different users dependon both the wireless channel conditions and the task load at the AP. Numericalresults demonstrate the benefit of the proposed joint-WPT-MEC design overalternative benchmark schemes without such joint design.

Optimal Resource Allocation for Wireless Powered Mobile Edge Computing with Dynamic Task Arrivals