# A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System

arXiv: https://arxiv.org/abs/2307.08474 · HTML: https://mlsanity.com/paper/2307.08474/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twin](https://mlsanity.com/paper/1907.01523/) (2019)
- [Learning-Based Computation Offloading for IoT Devices with Energy Harvesting](https://mlsanity.com/paper/1712.08768/) (2017)
- [Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2005.00938/) (2020)
- [Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.00514/) (2018)
- [Deep Learning Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks](https://mlsanity.com/paper/1911.09598/) (2019)
- [UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design](https://mlsanity.com/paper/1802.03906/) (2018)
- [UAV-Assisted Intelligent Reflecting Surface Symbiotic Radio System](https://mlsanity.com/paper/2007.14029/) (2020)
- [Risk-Based Optimization of Virtual Reality over Terahertz Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2002.09052/) (2020)
- [UAV-Assisted and Intelligent Reflecting Surfaces-Supported Terahertz Communications](https://mlsanity.com/paper/2010.14223/) (2020)

## Built on by (most cited first, 2 of 2)

- [Selective Task offloading for Maximum Inference Accuracy and Energy efficient Real-Time IoT Sensing Systems](https://mlsanity.com/paper/2402.16904/) (2024)
- [IGADA-IoT: IoT Sensor Energy Optimization in Wireless Sensor Networks Driven by Automatic Data Augmentation](https://mlsanity.com/paper/2605.27397/) (2026)

## Abstract (arXiv)

Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient offloading of computational tasks from user electronic devices to Unmanned Aerial Vehicles or local execution. For the generation of high-quality task-offloading allocations, conventional numerical optimization methods often struggle to solve challenging combinatorial optimization problems within the limited channel coherence time, thereby failing to respond quickly to dynamic changes in system conditions. To address this challenge, we propose a deep learning-based optimization framework called Iterative Order-Preserving policy Optimization (IOPO), which enables the generation of energy-efficient task-offloading decisions within milliseconds. Unlike exhaustive search methods, IOPO provides continuous updates to the offloading decisions without resorting to exhaustive search, resulting in accelerated convergence and reduced computational complexity, particularly when dealing with complex problems characterized by extensive solution spaces. Experimental results demonstrate that the proposed framework can generate energy-efficient task-offloading decisions within a very short time period, outperforming other benchmark methods.