The next generations of cellular networks will follow the Open Radio Access Network (RAN) paradigm, which promotes openness, virtualization, programmability, and data-driven control loops in the mobile environment. This will help network operators support new bespoke services on the same physical infrastructure, thanks to the flexibility and reconfigurability of software-based deployments and algorithmic control. Open RAN will also decrease operational costs because of the increased efficiency enabled by virtualized and open ecosystems. This open and disaggregated architecture also enables the practical deployment of artificial intelligence and machine learning solutions at scale to perform, for instance inference and traffic forecasting, or to configure RAN nodes based on run-time conditions and traffic requirements.

Specifically, we explore the Open RAN ecosystem and, leveraging the architecture and open interfaces standardized by the O-RAN Alliance, we propose innovative approaches for the control and performance optimization of the next generation of cellular networks.

We provide 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 provide a critical perspective on the limitations of the state-of-the-art, as well as feasible directions toward fully open source, programmable 5G networks.

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We present the first detailed tutorial on O-RAN. We also discuss the main research challenges and review early results. We provide a deep dive on the O-RAN specifications, describing its architecture, design principles, and the O-RAN interfaces. We then describe how the O-RAN RAN Intelligent Controllers (RICs) can be used to effectively control and manage 3GPP-defined RANs. Based on this, we discuss innovations and challenges that relate to O-RAN networks, including the Artificial Intelligence (AI) and Machine Learning (ML) workflows that the architecture and interfaces enable, and security and standardization issues. Finally, we review experimental research platforms that can be used to design and test O-RAN networks, along with recent research results, and we outline future directions for O-RAN development.

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We propose OpenRAN Gym, a practical, open, experimental toolbox that provides end-to-end design, data collection, and testing workflows for intelligent control in next generation Open RAN systems. OpenRAN Gym builds on software frameworks for the collection of large datasets and RAN control, and on a lightweight O-RAN environment for experimental wireless platforms. We first provide an overview of OpenRAN Gym and then describe how it can be used to collect data, to design and train artificial intelligence and machine learning-based ORAN applications (xApps), and to test xApps on a softwarized RAN. Then, we provide an example of two xApps designed with OpenRAN Gym and used to control a large-scale network with 7 base stations and 42 users deployed on the Colosseum testbed. OpenRAN Gym and its software components are open source and publicly-available to the research community.

We propose ColORAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a “wireless data factory”. Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. Extensive results from our first-of-its-kind large-scale evaluation highlight the benefits and challenges of DRL-based adaptive control. They also provide insights on the development of wireless DRL pipelines, from data analysis to the design of DRL agents, and on the tradeoffs associated to training on a live RAN. ColO-RAN and the collected largescale dataset will be made publicly available to the research community.

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We propose SCOPE, an open and softwarized prototyping platform for NextG systems. SCOPE is made up of: (i) A ready-to-use, portable opensource container for instantiating softwarized and programmable cellular network elements (e.g., base stations and users); (ii) an emulation module for diverse real-world deployments, channels and traffic conditions for testing new solutions; (iii) a data collection module for artificial intelligence and machine learning-based applications, and (iv) a set of open APIs for users to control network element functionalities in real time. Researchers can use SCOPE to test and validate NextG solutions over a variety of large-scale scenarios before implementing them on commercial infrastructures. We demonstrate the capabilities of SCOPE and its platform independence by prototyping exemplary cellular solutions in the controlled environment of Colosseum, the world’s largest wireless network emulator. We then port these solutions to indoor and outdoor testbeds, namely, to Arena and POWDER, a PAWR platform.

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We present OrchestRAN, a novel orchestration framework for next generation systems that embraces and builds upon the Open Radio Access Network (RAN) paradigm to provide a practical solution to these challenges. OrchestRAN has been designed to execute in the non-Real-time (RT) RAN Intelligent Controller (RIC) and allows Telcos to specify high-level control/inference objectives (i.e., adapt scheduling, and forecast capacity in near-RT, e.g., for a set of base stations in Downtown New York). OrchestRAN automatically computes the optimal set of data-driven algorithms and their execution location (e.g., in the cloud, or at the edge) to achieve intents specified by the Telcos while meeting the desired timing requirements and avoiding conflicts between different data-driven algorithms controlling the same parameters set. We show that the problem of orchestrating intelligence in Open RAN is NP-hard, and design low-complexity solutions to support real-world applications. We prototype OrchestRAN and test it at scale on Colosseum, the world’s largest wireless network emulator with hardware in the loop. Our experimental results on a network with 7 base stations and 42 users demonstrate that OrchestRAN is able to instantiate data-driven services on demand with minimal control overhead and latency.

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We propose the Channel-Aware Reactive Mechanism (ChARM), a data-driven O-RAN-compliant framework that allows (i) sensing the spectrum to infer the presence of interference and (ii) reacting in real time by switching the distributed unit (DU) and radio unit (RU) operational parameters according to a specified spectrum access policy. ChARM is based on neural networks operating directly on unprocessed I/Q waveforms to determine the current spectrum context. ChARM does not require any modification to the existing 3GPP standards. It is designed to operate within the O-RAN specifications, and can be used in conjunction with other spectrum sharing mechanisms (e.g., LTEU, LTE-LAA or MulteFire). We demonstrate the performance of ChARM in the context of spectrum sharing among LTE and Wi- Fi in unlicensed bands, where a controller operating over a RAN Intelligent Controller (RIC) senses the spectrum and switches cell frequency to avoid Wi-Fi. We develop a prototype of ChARM using srsRAN, and leverage the Colosseum channel emulator to collect a large-scale waveform dataset to train our neural networks with. To collect standard-compliant Wi-Fi data, we extended the Colosseum testbed using system-on-chip (SoC) boards running a modified version of the OpenWiFi architecture. Experimental results show that ChARM achieves accuracy of up to 96% on Colosseum and 85% on an over-the-air testbed, demonstrating the capacity of ChARM to exploit the considered spectrum channels.

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We present QCell, a Deep Q-Networkbased optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference conditions and traffic patterns. QCell is prototyped on Colosseum, the world’s largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user’s throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency.

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We provide the first large-scale demonstration of a the integration of O-RAN-compliant software components with an open-source full-stack softwarized cellular network. Experiments conducted on Colosseum, the world's largest wireless network emulator, demonstrate closed-loop integration of real-time analytics and control through deep reinforcement learning agents. We also demonstrate for the first time Radio Access Network (RAN) control through xApps running on the near real-time RAN Intelligent Controller (RIC), to optimize the scheduling policies of co-existing network slices, leveraging O-RAN open interfaces to collect data at the edge of the network.

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We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone's location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on an outdoor multi-cell RAN testbed. Furthermore, we perform a large-scale simulation assessment of the proposed system on the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN-based control achieves an average 19% network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

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We introduce ns-O-RAN, a software integration between a real-world near-real-time RIC and an ns-3 simulated RAN which provides a platform for researchers and telco operators to build, test and integrate xApps. ns-O-RAN extends a popular Open RAN experimental framework (OpenRAN Gym) with simulation capabilities that enable the generation of realistic datasets without the need for experimental infrastructure. We implement it as a new open-source ns-3 module that uses the E2 interface to connect different simulated 5G base stations with the RIC, enabling the exchange of E2 messages and RAN Key Performance Measurements (KPMs) to be consumed by standard xApps. Furthermore, we test ns-O-RAN with the O-RAN Software Community (OSC) and OpenRAN Gym near-RT RIC, simplifying the onboarding from a test environment to production with real telecom hardware controlled without major reconfigurations required. ns-O-RAN is open source and publicly available, together with quick-start tutorials and documentation.

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