# Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading

arXiv: https://arxiv.org/abs/1708.08810 · HTML: https://mlsanity.com/paper/1708.08810/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2017.

## Built on by (most cited first, 10 of 10)

- [Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge](https://mlsanity.com/paper/2407.09873/) (2024)
- [Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing](https://mlsanity.com/paper/2305.19558/) (2023)
- [Multi-objective Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2307.14346/) (2023)
- [Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation](https://mlsanity.com/paper/2402.16866/) (2024)
- [Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing](https://mlsanity.com/paper/2509.10474/) (2025)
- [Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2206.05881/) (2022)
- [Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint](https://mlsanity.com/paper/2109.05767/) (2021)
- [QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2311.02525/) (2023)
- [QAROO: AI-Driven Online Task Offloading for Energy-Efficient and Sustainable MEC Networks](https://mlsanity.com/paper/2604.25740/) (2026)
- [Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation](https://mlsanity.com/paper/2604.25980/) (2026)

## Abstract (arXiv)

In this paper, we consider a multi-user mobile edge computing (MEC) network powered by wireless power transfer (WPT), where each energy-harvesting WD follows a binary computation offloading policy, i.e., data set of a task has to be executed as a whole either locally or remotely at the MEC server via task offloading. In particular, we are interested in maximizing the (weighted) sum computation rate of all the WDs in the network by jointly optimizing the individual computing mode selection (i.e., local computing or offloading) and the system transmission time allocation (on WPT and task offloading). The major difficulty lies in the combinatorial nature of multi-user computing mode selection and its strong coupling with transmission time allocation. To tackle this problem, we first consider a decoupled optimization, where we assume that the mode selection is given and propose a simple bi-section search algorithm to obtain the conditional optimal time allocation. On top of that, a coordinate descent method is devised to optimize the mode selection. The method is simple in implementation but may suffer from high computational complexity in a large-size network. To address this problem, we further propose a joint optimization method based on the ADMM (alternating direction method of multipliers) decomposition technique, which enjoys much slower increase of computational complexity as the networks size increases. Extensive simulations show that both the proposed methods can efficiently achieve near-optimal performance under various network setups, and significantly outperform the other representative benchmark methods considered.