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Leonardo Bonati’s PhD Dissertation

July 13, 2022 @ 11:00 am - 12:00 pm

“Softwarized Approaches for the Open RAN of NextG Cellular Networks”


The 5th and 6th generations of cellular networks (5G and 6G), also known as NextG, will bring unprecedented flexibility to the wireless cellular ecosystem. Because of a typically closed and rigid market, the telco industry has incurred high costs and non-trivial obstacles for delivering new services and functionalities that satisfy the requirements and the demands of NextG networks. To break this trend the industry is now moving toward open architectures based on softwarized approaches, which afford network operators flexible control and unprecedented adaptability to heterogeneous conditions, including traffic and application requirements. Now, by simply expressing a high-level intent, operators will be able to instantiate bespoke services on-demand on a generic hardware infrastructure, and to adapt such services to the current network conditions. Through disaggregation, network elements will split their functionalities across multiple components—possibly provided by different vendors—interconnected through well-defined open interfaces. The separation of control functions from the hardware fabric, and the introduction of standardized control interfaces, will ultimately enable the definition and use of softwarized control loops, which will bring embedded intelligence and real-time analytics to effectively realizing the vision of autonomous and self-optimizing networks.
This dissertation work focuses on the design, prototyping and experimental evaluation of softwarized approaches for the Open Radio Access Network (RAN) of NextG cellular networks. We analyze the architectural enablers, challenges, and requirements for a programmatic zero-touch control of the very many network elements and propose practical solutions for its realization. We prototype solutions by leveraging open-source software implementations of cellular protocol stacks and frameworks, and heterogeneous virtualization technologies, including the srsRAN and OpenAirInterface cellular implementations, and the O-RAN framework. The contributions of this work include (i) the first demonstration of O-RAN data-driven control loops in a large-scale experimental testbed using open-source, programmable RAN and RAN Intelligent Controller (RIC) components through xApps of our design; (ii) CellOS, a zero-touch cellular operating system that automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices starting from a high-level intent expressed by the operators; (iii) OpenRAN Gym, the first publicly-available research platform for the design, prototyping, and experimentation at scale of data-driven O-RAN solutions, and (iv) OrchestRAN, a network intelligence orchestration framework for Open RAN that automates the deployment of data-driven inference and control solutions. The effectiveness of our solutions in achieving superior control and performance of the RAN is demonstrated at scale on state-of-the-art experimental facilities, including software-defined radio-based laboratory setups and open access experimental wireless platforms, such as Colosseum, Arena, and the POWDER and COSMOS platforms from the U.S. PAWR program.


Electrical and Computer Engineering
MS/PhD Thesis Defense
Faculty, Staff


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360 Huntington Ave
Boston, MA 02115 United States
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