# Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing

arXiv: https://arxiv.org/abs/2203.09954 · HTML: https://mlsanity.com/paper/2203.09954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks](https://mlsanity.com/paper/1907.03289/) (2019)

## Built on by (most cited first, 3 of 3)

- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Digital Twin Assisted Deep Reinforcement Learning for Online Admission Control in Sliced Network](https://mlsanity.com/paper/2310.09299/) (2023)
- [Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach](https://mlsanity.com/paper/2401.00443/) (2023)

## Abstract (arXiv)

In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach can be applied to solve this problem, a huge burden of computational complexity arises which limits the application of BnB. To address this issue, we propose an intelligent BnB (IBnB) approach which applies deep learning (DL) to learn the pruning strategy of the BnB approach. By using this learning scheme, the structure of the BnB approach ensures near-optimal performance and meanwhile DL-based pruning strategy significantly reduces the complexity. Numerical results verify that the proposed IBnB approach achieves optimal performance with complexity reduced by over 80%.