OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms

• URL: http://arxiv.org/abs/2207.12362v1
• Date: Mon, 25 Jul 2022 17:22:25 GMT
• Title: OpenRAN Gym: AI/ML Development, Data Collection, and Testing for O-RAN on PAWR Platforms
• Authors: Leonardo Bonati, Michele Polese, Salvatore D'Oro, Stefano Basagni, Tommaso Melodia
• Abstract summary: OpenRAN Gym is a unified, open, and O-RAN-compliant experimental toolbox for data collection, design, prototyping and testing of end-to-end data-driven control solutions. OpenRAN Gym and its software components are open-source and publicly-available to the research community.
• Score: 28.37831674645226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open Radio Access Network (RAN) architectures will enable interoperability, openness and programmable data-driven control in next generation cellular networks. However, developing and testing efficient solutions that generalize across heterogeneous cellular deployments and scales, and that optimize network performance in such diverse environments is a complex task that is still largely unexplored. In this paper we present OpenRAN Gym, a unified, open, and O-RAN-compliant experimental toolbox for data collection, design, prototyping and testing of end-to-end data-driven control solutions for next generation Open RAN systems. OpenRAN Gym extends and combines into a unique solution several software frameworks for data collection of RAN statistics and RAN control, and a lightweight O-RAN near-real-time RAN Intelligent Controller (RIC) tailored to run on experimental wireless platforms. We first provide an overview of the various architectural components of OpenRAN Gym and describe how it is used to collect data and design, train and test artificial intelligence and machine learning O-RAN-compliant applications (xApps) at scale. We then describe in detail how to test the developed xApps on softwarized RANs and provide an example of two xApps developed with OpenRAN Gym that are used to control a network with 7 base stations and 42 users deployed on the Colosseum testbed. Finally, we show how solutions developed with OpenRAN Gym on Colosseum can be exported to real-world, heterogeneous wireless platforms, such as the Arena testbed and the POWDER and COSMOS platforms of the PAWR program. OpenRAN Gym and its software components are open-source and publicly-available to the research community.

Related papers

• Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs [49.71319907864573]
  In this paper, we propose multi-agent skill discovery which enables the ease of decomposition. Our key idea is to approximate the joint state space as a Kronecker graph, based on which we can directly estimate its Fiedler vector. Considering that directly computing the Laplacian spectrum is intractable for tasks with infinite-scale state spaces, we further propose a deep learning extension of our method.
  arXiv  Detail & Related papers  (2023-07-21T14:53:12Z)
• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)
• Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures [16.48682480842328]
  5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. We propose an open architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the user level.
  arXiv  Detail & Related papers  (2022-09-28T15:31:06Z)
• Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis [3.8073142980733]
  Open Radio Access Network (O-RAN) has introduced an emerging RAN architecture that enables openness, intelligence, and automated control. The RAN Intelligent Controller (RIC) provides the platform to design and deploy RAN controllers. xApps are the applications which will take this responsibility by leveraging machine learning (ML) algorithms and acting in near-real time.
  arXiv  Detail & Related papers  (2022-09-26T19:12:18Z)
• Intelligent Closed-loop RAN Control with xApps in OpenRAN Gym [28.37831674645226]
  We discuss how to design AI/ML solutions for the intelligent closed-loop control of the Open RAN. We show how to embed these solutions into xApps instantiated on the O-RAN near-real-time RAN Intelligent Controller (RIC) through OpenRAN Gym.
  arXiv  Detail & Related papers  (2022-08-31T14:09:12Z)
• Composing Complex and Hybrid AI Solutions [52.00820391621739]
  We describe an extension of the Acumos system towards enabling the above features for general AI applications. Our extensions include support for more generic components with gRPC/Protobuf interfaces. We provide examples of deployable solutions and their interfaces.
  arXiv  Detail & Related papers  (2022-02-25T08:57:06Z)
• OrchestRAN: Network Automation through Orchestrated Intelligence in the Open RAN [27.197110488665157]
  We present and prototyping OrchestRAN, a novel orchestration framework for network intelligence. OrchestRAN has been designed to execute in the non-real-time RAN Intelligent Controller (RIC) and allows Network Operators (NOs) to specify high-level control/inference objectives. We show that the problem of orchestrating intelligence in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications.
  arXiv  Detail & Related papers  (2022-01-14T19:20:34Z)
• ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms [22.260874168813647]
  ColO-RAN is the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a " wireless data factory" Extensive results from our first-of-its-kind large-scale evaluation highlight the benefits and challenges of DRL-based adaptive control.
  arXiv  Detail & Related papers  (2021-12-17T15:14:22Z)
• Anchor-free Oriented Proposal Generator for Object Detection [59.54125119453818]
  Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. We propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons the horizontal boxes-related operations from the network architecture.
  arXiv  Detail & Related papers  (2021-10-05T10:45:51Z)
• Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
  We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN) Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
  arXiv  Detail & Related papers  (2021-03-08T21:48:55Z)
• Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets [70.62568022925971]
  generative adversarial networks (GANs) must be fed by large datasets that adequately represent the data space. In many scenarios, the available datasets may be limited and distributed across multiple agents, each of which is seeking to learn the distribution of the data on its own. In this paper, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner.
  arXiv  Detail & Related papers  (2020-02-02T02:58:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.