arXiv subjects

Gabriele Gemmi

Publications and source records attributed to Gabriele Gemmi.

10 recordsLinked to original sources

arXiv2026

GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing

Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening against field anomalies and diverse deployment environments; (iv) data-driven optimization of network functionalities; (v) discovering and prototyping novel waveforms, functionalities, and capabilities for future standards; and (vi) securing the stack against vulnerabilities. Although Large Language Models (LLMs) have compressed comparable R&D work in general software engineering from days to minutes, their known pitfalls worsen on Radio Access Network (RAN) use cases: they hallucinate Application Programming Interfaces (APIs) and mis-read specifications, which kills interoperability of RAN components at the first mistake, and they heavily rely on simulations for designing algorithms, which is notorious for breaking when transferred to real hardware. To address these challenges, we present GENESIS, an agentic Artificial Intelligence (AI) framework that converts intents (e.g., a specification clause, a telemetry anomaly, or a research hypothesis) into solutions validated with over-the-air experiments, fed back into a persistent knowledge base. GENESIS is built on three composable primitives (agents, skills, hooks) and a knowledge layer (SYNAPSE) that doubles as the source of ground truth and the recipient of every artifact the framework produces, making capabilities compound across runs.

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arXiv2026

A Techno-Economic Framework for Cost Modeling and Revenue Opportunities in Open and Programmable AI-RAN

The large-scale deployment of 5G networks has not delivered the expected return on investment for mobile network operators, raising concerns about the economic viability of future 6G rollouts. At the same time, surging demand for Artificial Intelligence (AI) inference and training workloads is straining global compute capacity. AI-RAN architectures, in which Radio Access Network (RAN) platforms accelerated on Graphics Processing Unit (GPU) share idle capacity with AI workloads during off-peak periods, offer a potential path to improved capital efficiency. However, the economic case for such systems remains unsubstantiated. In this paper, we present a techno-economic analysis of AI-RAN deployments by combining publicly available benchmarks of 5G Layer-1 processing on heterogeneous platforms -- from x86 servers with accelerators for channel coding to modern GPUs -- with realistic traffic models and AI service demand profiles for Large Language Model (LLM) inference. We construct a joint cost and revenue model that quantifies the surplus compute capacity available in GPU-based RAN deployments and evaluates the returns from leasing it to AI tenants. Our results show that, across a range of scenarios encompassing token depreciation, varying demand dynamics, and diverse GPU serving densities, the additional capital and operational expenditures of GPU-heavy deployments are offset by AI-on-RAN revenue, yielding a return on investment of up to 8x. These findings strengthen the long-term economic case for accelerator-based RAN architectures and future 6G deployments.

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arXiv2025

Joint Routing, Resource Allocation, and Energy Optimization for Integrated Access and Backhaul with Open RAN

As networks evolve towards 6G, Mobile Network Operators (MNOs) must accommodate diverse requirements and at the same time manage rising energy consumption. Integrated Access and Backhaul (IAB) networks facilitate dense cellular deployments with reduced infrastructure complexity. However, the multi-hop wireless backhauling in IAB networks necessitates proper routing and resource allocation decisions to meet the performance requirements. At the same time, cell densification makes energy optimization crucial. This paper addresses the joint optimization of routing and resource allocation in IAB networks through two distinct objectives: energy minimization and throughput maximization. We develop a novel capacity model that links power levels to achievable data rates. We propose two practical large-scale approaches to solve the optimization problems and leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to integrate the solutions. The approaches are evaluated on diverse scenarios built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed approaches effectively reduces number of activated nodes to save energy and achieves approximately 100 Mbps of minimum data rate per User Equipment (UE) during peak hours of the day using spectrum within the Frequency Range (FR) 3, or upper midband. The results validate the practical applicability of our framework for next-generation IAB network deployment and optimization.

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arXiv2024

Optimizing and Managing Wireless Backhaul for Resilient Next-Generation Cellular Networks

Next-generation wireless networks target high network availability, ubiquitous coverage, and extremely high data rates for mobile users. This requires exploring new frequency bands, e.g., mmWaves, moving toward ultra-dense deployments in urban locations, and providing ad hoc, resilient connectivity in rural scenarios. The design of the backhaul network plays a key role in advancing how the access part of the wireless system supports next-generation use cases. Wireless backhauling, such as the newly introduced Integrated Access and Backhaul (IAB) concept in 5G, provides a promising solution, also leveraging the mmWave technology and steerable beams to mitigate interference and scalability issues. At the same time, however, managing and optimizing a complex wireless backhaul introduces additional challenges for the operation of cellular systems. This paper presents a strategy for the optimal creation of the backhaul network considering various constraints related to network topology, robustness, and flow management. We evaluate its feasibility and efficiency using synthetic and realistic network scenarios based on 3D modeling of buildings and ray tracing. We implement and prototype our solution as a dynamic IAB control framework based on the Open Radio Access Network (RAN) architecture, and demonstrate its functionality in Colosseum, a large-scale wireless network emulator with hardware in the loop.

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arXiv2024

Consistent and Repeatable Testing of O-RAN Distributed Unit (O-DU) across Continents

Open Radio Access Networks (O-RAN) are expected to revolutionize the telecommunications industry with benefits like cost reduction, vendor diversity, and improved network performance through AI optimization. Supporting the O-RAN ALLIANCE's mission to achieve more intelligent, open, virtualized and fully interoperable mobile networks, O-RAN Open Testing and Integration Centers (OTICs) play a key role in accelerating the adoption of O-RAN specifications based on rigorous testing and validation. One theme in the recent O-RAN Global PlugFest Spring 2024 focused on demonstrating consistent and repeatable Open Fronthaul testing in multiple labs. To respond to this topic, in this paper, we present a detailed analysis of the testing methodologies and results for O-RAN Distributed Unit (O-DU) in O-RAN across two OTICs. We identify key differences in testing setups, share challenges encountered, and propose best practices for achieving repeatable and consistent testing results. Our findings highlight the impact of different deployment technologies and testing environments on performance and conformance testing outcomes, providing valuable insights for future O-RAN implementations.

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arXiv2024

Open6G OTIC: A Blueprint for Programmable O-RAN and 3GPP Testing Infrastructure

Softwarized and programmable Radio Access Networks (RANs) come with virtualized and disaggregated components, increasing the supply chain robustness and the flexibility and dynamism of the network deployments. This is a key tenet of Open RAN, with open interfaces across disaggregated components specified by the O-RAN ALLIANCE. It is mandatory, however, to validate that all components are compliant with the specifications and can successfully interoperate, without performance gaps with traditional, monolithic appliances. Open Testing & Integration Centers (OTICs) are entities that can verify such interoperability and adherence to the standard through rigorous testing. However, how to design, instrument, and deploy an OTIC which can offer testing for multiple tenants, heterogeneous devices, and is ready to support automated testing is still an open challenge. In this paper, we introduce a blueprint for a programmable OTIC testing infrastructure, based on the design and deployment of the Open6G OTIC at Northeastern University, Boston, and provide insights on technical challenges and solutions for O-RAN testing at scale.

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arXiv2024

ColosSUMO: Evaluating Cooperative Driving Applications with Colosseum

The quest for safer and more efficient transportation through cooperative, connected and automated mobility (CCAM) calls for realistic performance analysis tools, especially with respect to wireless communications. While the simulation of existing and emerging communication technologies is an option, the most realistic results can be obtained by employing real hardware, as done for example in field operational tests (FOTs). For CCAM, however, performing FOTs requires vehicles, which are generally expensive. and performing such tests can be very demanding in terms of manpower, let alone considering safety issues. Mobility simulation with hardware-in-the-loop (HIL) serves as a middle ground, but current solutions lack flexibility and reconfigurability. This work thus proposes ColosSUMO as a way to couple Colosseum, the world's largest wireless network emulator, with the SUMO mobility simulator, showing its design concept, how it can be exploited to simulate realistic vehicular environments, and its flexibility in terms of communication technologies.

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arXiv2024

Colosseum: The Open RAN Digital Twin

Recent years have witnessed the Open Radio Access Network (RAN) paradigm transforming the fundamental ways cellular systems are deployed, managed, and optimized. This shift is led by concepts such as openness, softwarization, programmability, interoperability, and intelligence of the network, all of which had never been applied to the cellular ecosystem before. The realization of the Open RAN vision into practical architectures, intelligent data-driven control loops, and efficient software implementations, however, is a multifaceted challenge, which requires (i) datasets to train Artificial Intelligence (AI) and Machine Learning (ML) models; (ii) facilities to test models without disrupting production networks; (iii) continuous and automated validation of the RAN software; and (iv) significant testing and integration efforts. This paper poses itself as a tutorial on how Colosseum - the world's largest wireless network emulator with hardware in the loop - can provide the research infrastructure and tools to fill the gap between the Open RAN vision, and the deployment and commercialization of open and programmable networks. We describe how Colosseum implements an Open RAN digital twin through a high-fidelity Radio Frequency (RF) channel emulator and end-to-end softwarized O-RAN and 5G-compliant protocol stacks, thus allowing users to reproduce and experiment upon topologies representative of real-world cellular deployments. Then, we detail the twinning infrastructure of Colosseum, as well as the automation pipelines for RF and protocol stack twinning. Finally, we showcase a broad range of Open RAN use cases implemented on Colosseum, including the real-time connection between the digital twin and real-world networks, and the development, prototyping, and testing of AI/ML solutions for Open RAN.

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arXiv2023

Joint Routing and Energy Optimization for Integrated Access and Backhaul with Open RAN

Energy consumption represents a major part of the operating expenses of mobile network operators. With the densification foreseen with 5G and beyond, energy optimization has become a problem of crucial importance. While energy optimization is widely studied in the literature, there are limited insights and algorithms for energy-saving techniques for Integrated Access and Backhaul (IAB), a self-backhauling architecture that ease deployment of dense cellular networks reducing the number of fiber drops. This paper proposes a novel optimization model for dynamic joint routing and energy optimization in IAB networks. We leverage the closed-loop control framework introduced by the Open Radio Access Network (O-RAN) architecture to minimize the number of active IAB nodes while maintaining a minimum capacity per User Equipment (UE). The proposed approach formulates the problem as a binary nonlinear program, which is transformed into an equivalent binary linear program and solved using the Gurobi solver. The approach is evaluated on a scenario built upon open data of two months of traffic collected by network operators in the city of Milan, Italy. Results show that the proposed optimization model reduces the RAN energy consumption by 47%, while guaranteeing a minimum capacity for each UE.

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arXiv2023

Toward Open Integrated Access and Backhaul with O-RAN

Millimeter wave (mmWave) communications has been recently standardized for use in the fifth generation (5G) of cellular networks, fulfilling the promise of multi-gigabit mobile throughput of current and future mobile radio network generations. In this context, the network densification required to overcome the difficult mmWave propagation will result in increased deployment costs. Integrated Access and Backhaul (IAB) has been proposed as an effective mean of reducing densification costs by deploying a wireless mesh network of base stations, where backhaul and access transmissions share the same radio technology. However, IAB requires sophisticated control mechanisms to operate efficiently and address the increased complexity. The Open Radio Access Network (RAN) paradigm represents the ideal enabler of RAN intelligent control, but its current specifications are not compatible with IAB. In this work, we discuss the challenges of integrating IAB into the Open RAN ecosystem, detailing the required architectural extensions that will enable dynamic control of 5G IAB networks. We implement the proposed integrated architecture into the first publicly-available Open-RAN-enabled experimental framework, which allows prototyping and testing Open-RAN-based solutions over end-to-end 5G IAB networks. Finally, we validate the framework with both ideal and realistic deployment scenarios exploiting the large-scale testing capabilities of publicly available experimental platforms

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