# Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2507.19712 · HTML: https://mlsanity.com/paper/2507.19712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 80 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 16 of 16)

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- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
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- [Intelligent Transportation Systems Using External Infrastructure: A Literature Survey](https://mlsanity.com/paper/2112.05615/) (2021)
- [Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing](https://mlsanity.com/paper/1812.08076/) (2018)
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- [AI Testing Framework for Next-G O-RAN Networks: Requirements, Design, and Research Opportunities](https://mlsanity.com/paper/2211.03979/) (2022)
- [Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework](https://mlsanity.com/paper/2302.02711/) (2023)
- [Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN](https://mlsanity.com/paper/2308.07242/) (2023)
- [Joint Optimization of Completion Ratio and Latency of Offloaded Tasks with Multiple Priority Levels in 5G Edge](https://mlsanity.com/paper/2407.11155/) (2024)
- [Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges](https://mlsanity.com/paper/2410.23086/) (2024)
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## Abstract (arXiv)

In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing. Existing studies often overlook the intricate interdependencies between missions and the costs associated with offloading tasks to edge servers, leading to suboptimal decision-making. To bridge this gap, we introduce Oranits, a novel system model that explicitly accounts for mission dependencies and offloading costs while optimizing performance through vehicle cooperation. To achieve this, we propose a twofold optimization approach. First, we develop a metaheuristic-based evolutionary computing algorithm, namely the Chaotic Gaussian-based Global ARO (CGG-ARO), serving as a baseline for one-slot optimization. Second, we design an enhanced reward-based deep reinforcement learning (DRL) framework, referred to as the Multi-agent Double Deep Q-Network (MA-DDQN), that integrates both multi-agent coordination and multi-action selection mechanisms, significantly reducing mission assignment time and improving adaptability over baseline methods. Extensive simulations reveal that CGG-ARO improves the number of completed missions and overall benefit by approximately 7.1% and 7.7%, respectively. Meanwhile, MA-DDQN achieves even greater improvements of 11.0% in terms of mission completions and 12.5% in terms of the overall benefit. These results highlight the effectiveness of Oranits in enabling faster, more adaptive, and more efficient task processing in dynamic ITS environments.