# Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead

arXiv: https://arxiv.org/abs/2005.10027 · HTML: https://mlsanity.com/paper/2005.10027/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 15 of 15)

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [Colosseum: Large-Scale Wireless Experimentation Through Hardware-in-the-Loop Network Emulation](https://mlsanity.com/paper/2110.10617/) (2021)
- [OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN](https://mlsanity.com/paper/2201.05632/) (2022)
- [Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples](https://mlsanity.com/paper/2207.01531/) (2022)
- [OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms](https://mlsanity.com/paper/2207.12362/) (2022)
- [Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul](https://mlsanity.com/paper/2211.07466/) (2022)
- [Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym](https://mlsanity.com/paper/2208.14877/) (2022)
- [Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp](https://mlsanity.com/paper/2505.01816/) (2025)
- [Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs](https://mlsanity.com/paper/2301.00597/) (2023)
- [AI-Native Open RAN for Non-Terrestrial Networks: An Overview](https://mlsanity.com/paper/2507.11935/) (2025)
- [AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation](https://mlsanity.com/paper/2606.08173/) (2026)
- [4C: A Computation, Communication, and Control Co-Design Framework for CAVs](https://mlsanity.com/paper/2107.01142/) (2021)
- [Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning](https://mlsanity.com/paper/2207.06537/) (2022)
- [A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration](https://mlsanity.com/paper/2312.16142/) (2023)

## Abstract (arXiv)

Fifth generation (5G) cellular networks will serve a wide variety of heterogeneous use cases, including mobile broadband users, ultra-low latency services and massively dense connectivity scenarios. The resulting diverse communication requirements will demand networking with unprecedented flexibility, not currently provided by the monolithic black-box approach of 4G cellular networks. The research community and an increasing number of standardization bodies and industry coalitions have recognized softwarization, virtualization, and disaggregation of networking functionalities as the key enablers of the needed shift to flexibility. Particularly, software-defined cellular networks are heralded as the prime technology to satisfy the new application-driven traffic requirements and to support the highly time-varying topology and interference dynamics, because of their openness through well-defined interfaces, and programmability, for swift and responsive network optimization. Leading the technological innovation in this direction, several 5G software-based projects and alliances have embraced the open source approach, making new libraries and frameworks available to the wireless community. This race to open source softwarization, however, has led to a deluge of solutions whose interoperability and interactions are often unclear. This article provides the first cohesive and exhaustive compendium of recent open source software and frameworks for 5G cellular networks, with a full stack and end-to-end perspective. We detail their capabilities and functionalities focusing on how their constituting elements fit the 5G ecosystem, and unravel the interactions among the surveyed solutions. Finally, we review hardware and testbeds on which these frameworks can run, and discuss the limitations of the state-of-the-art, as well as feasible directions toward fully open source, programmable 5G networks.