Computation Rate Maximization for Wireless Powered Mobile Edge Computing

TL;DR

This paper proposes an optimized design for a wireless powered multiuser MEC system that maximizes computation rates by jointly optimizing energy transmission, task partitioning, and time allocation, demonstrating significant performance improvements.

Contribution

It introduces a joint optimization framework for energy beamforming, task partitioning, and time allocation in wireless powered MEC systems, with a semi-closed form solution.

Findings

Optimized energy beamforming enhances computation rates.

Joint optimization outperforms benchmark schemes.

Numerical results validate the effectiveness of the proposed method.

Abstract

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.