# Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

arXiv: https://arxiv.org/abs/2201.11295 · HTML: https://mlsanity.com/paper/2201.11295/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2022.

## Built on by (most cited first, 7 of 7)

- [Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration](https://mlsanity.com/paper/2307.09151/) (2023)
- [Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning](https://mlsanity.com/paper/2505.14581/) (2025)
- [Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks](https://mlsanity.com/paper/2507.06997/) (2025)
- [The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments](https://mlsanity.com/paper/2403.07923/) (2024)
- [Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks](https://mlsanity.com/paper/2604.01238/) (2026)
- [Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery](https://mlsanity.com/paper/2604.01049/) (2026)
- [When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System](https://mlsanity.com/paper/2605.20037/) (2026)

## Abstract (arXiv)

The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing (NS) are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and non-linear mathematical programming formulations, in this paper, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MEC-enabled IoV network based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable a better exploitation of the scarce channel resources. The considered problem addresses jointly the channel and power allocation, the slice selection and the vehicles selection (vehicles grouping). We model the problem as a single-agent Markov decision process. Then, we solve it using DRL using the well-known DQL algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.