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

arXiv: https://arxiv.org/abs/1907.07565 · HTML: https://mlsanity.com/paper/1907.07565/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing](https://mlsanity.com/paper/2202.12028/) (2022)
- [Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2312.00279/) (2023)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation](https://mlsanity.com/paper/2402.16866/) (2024)
- [Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency](https://mlsanity.com/paper/2504.20735/) (2025)
- [ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems](https://mlsanity.com/paper/2608.08804/) (2026)

## Abstract (arXiv)

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.