# Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks

arXiv: https://arxiv.org/abs/2501.06242 · HTML: https://mlsanity.com/paper/2501.06242/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network](https://mlsanity.com/paper/2009.10601/) (2020)
- [5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View](https://mlsanity.com/paper/1804.05057/) (2018)
- [Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.06439/) (2022)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2401.06922/) (2024)
- [Revisiting Wireless Internet Connectivity: 5G vs Wi-Fi 6](https://mlsanity.com/paper/2010.11601/) (2020)
- [A2-UAV: Application-Aware Content and Network Optimization of Edge-Assisted UAV Systems](https://mlsanity.com/paper/2301.06363/) (2023)

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

- [Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing](https://mlsanity.com/paper/2501.15203/) (2025)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)

## Abstract (arXiv)

5G technology enhances industries with high-speed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasing complexity of applications on User Equipment (UE), offloading resource-intensive tasks to robust servers is essential for improving latency and speed. The 3GPP's Multi-access Edge Computing (MEC) framework addresses this challenge by processing tasks closer to the user, highlighting the need for an intelligent controller to optimize task offloading and resource allocation. This paper introduces a novel methodology to efficiently allocate both communication and computational resources among individual UEs. Our approach integrates two critical 5G service imperatives: Ultra-Reliable Low Latency Communication (URLLC) and Massive Machine Type Communication (mMTC), embedding them into the decision-making framework. Central to this approach is the utilization of Proximal Policy Optimization, providing a robust and efficient solution to the challenges posed by the evolving landscape of 5G technology. The proposed model is evaluated in a simulated 5G MEC environment. The model significantly reduces processing time by 4% for URLLC users under strict latency constraints and decreases power consumption by 26% for mMTC users, compared to existing baseline models based on the reported simulation results. These improvements showcase the model's adaptability and superior performance in meeting diverse QoS requirements in 5G networks.