# Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems

## TL;DR

This paper develops optimal and heuristic online strategies for energy allocation and task offloading in wireless powered MEC systems, minimizing energy use while ensuring task completion under channel and task uncertainties.

## Contribution

It provides a closed-form optimal offline solution and proposes heuristic online algorithms for joint energy and task management in wireless powered MEC systems.

## Key findings

- Offline optimal solution derived in closed form.
- Heuristic online algorithms perform close to offline optimal.
- Joint design reduces energy consumption compared to benchmarks.

## Abstract

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.