arXiv subjects

Ilario Filippini

Publications and source records attributed to Ilario Filippini.

At least 19 recordsLinked to original sources

arXiv2026

RUN-O-RAN: An O-RAN-Native Architecture Enabling Cooperative Uplink Localization

Accurate positioning is increasingly required in indoor and dense urban environments; nonetheless, satellite-based systems are not always available, and standardized 5G localization solutions remain difficult to deploy with commercial devices. This paper presents RUN-O-RAN, an O-RAN-native framework that enables network-centric uplink localization using standard Sounding Reference Signal (SRS) transmissions from commercial 5G devices. RUN-O-RAN xApp coordinates serving and neighboring base stations, enabling non-serving base stations to retrieve SRS-based uplink timing measurements that would be unavailable in conventional RAN deployments, without modifying the UE or existing 3GPP signaling procedures. The framework combines cooperative SRS collection, first-path time-of-arrival estimation, timing-advance tracking, clock-drift compensation, and multi-anchor position estimation into a complete network-side localization service. Experimental evaluation over $150,000$ SRS transmissions validates the proposed framework, achieving meter-level localization under diverse propagation conditions while revealing the impact of anchor geometry and multipath on positioning accuracy. These findings demonstrate that cooperative SRS-based localization can be realized within the O-RAN ecosystem without modifying commercial UEs, providing a practical foundation for future network-native ISAC positioning services.

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arXiv2026

5 Shades of Cooperation: Spectrum Sharing in the Upper-Mid Band

Spectrum exclusively licensed to each mobile network operator (MNO) is scarce, and assigning it in fixed, frequency-orthogonal blocks leaves much of it idle under heterogeneous, time-varying traffic. Large antenna arrays offer an alternative: an operator can spend part of its spatial degrees of freedom (DoF) serving its own users and part suppressing interference toward others' users (nullforming), letting competing networks reuse the same band. This raises two questions: does trading DoF for interference suppression beat orthogonal partitioning, and how much coordination is needed to realize the gain? We cast inter-operator sharing as a continuum of cooperation "shades" of a single null-forming MU-MIMO precoding and interference-constrained scheduling primitive, spanning orthogonal partitioning, non-cooperative reuse, cooperative cross-operator protection, and a scheduling-aware bound exploiting foreign scheduling decisions. Evaluated on a ray-traced digital twin of a real multi-operator deployment with 27 base stations sharing a 7 GHz carrier, the cooperative shades deliver more than a 2.5$\times$ median per-user rate gain over both non-cooperative reuse and orthogonal splitting. Crucially, most of this gain requires only minimal inter-operator information exchange, indicating that a modest, standardizable metadata exchange, rather than tight joint processing, unlocks most of the value of shared-spectrum operation.

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arXiv2026

Toward Mobile and Converged Backhaul: The Promise of Wireless Access and Backhaul

Wireless Access and Backhaul (WAB) is emerging as a key enabler for flexible and cost-efficient 5G deployments, offering a modular architecture that decouples access and backhaul while supporting multi-technology and mobile backhaul links. This article introduces the WAB framework standardized in 3GPP Release 19, outlining its architecture and operational principles. A practical implementation built with commercial hardware and open-source software demonstrates the feasibility and efficiency of WAB systems. We further explore four representative application scenarios - ranging from on-demand coverage to mobile Software-Defined Wide Area Network (SD-WAN) connectivity - and discuss the technical challenges that must be addressed for large-scale adoption. These insights highlight WAB as a promising foundation for 5G-Advanced and a stepping stone toward future 6G networks.

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arXiv2026

Enabling Mobile Base Stations in 5G via Wireless Access Backhaul (WAB): A Multi-Band Experimental Study

Highly dynamic and mobile applications, such as vehicular networks, require stable connectivity, which is often challenging to achieve. Network densification is a key approach to address this issue and can be achieved cost-effectively through mobile base stations and wireless relaying. However, existing solutions rely on rigid and complex architectures that hinder deployment in dynamic scenarios. The recently standardized Wireless Access Backhaul (WAB) architecture represents a key evolution, enabling flexible and modular wireless relay networks with native support for mobility and multi-technology wireless backhaul. This paper presents the first experimental realization of a multi-band WAB testbed, combining an FR2 backhaul and an FR1 access link using open-source software and commercial off-the-shelf components. The proposed framework validates end-to-end WAB operation under mobility and demonstrates the extension of FR2 coverage while maintaining compatibility with legacy FR1 user equipment. Experimental campaigns in vehicular and outdoor-to-indoor scenarios confirm that WAB effectively mitigates FR2 limitations, particularly in uplink and Non-Line-of-Sight conditions. These results highlight WAB as a practical and scalable approach for vehicular and next-generation wireless networks.

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arXiv2025

AoA Services in 5G Networks: A Framework for Real-World Implementation and Systematic Testing

Accurate positioning is a key enabler for emerging 5G applications. While the standardized Location Management Function (LMF) operates centrally within the core network, its scalability and latency limitations hinder low-latency and fine-grained localization. A practical alternative is to shift positioning intelligence toward the radio access network (RAN), where uplink sounding reference signal (SRS)-based angle-of-arrival (AoA) estimation offers a lightweight, network-native solution. In this work, we present the first fully open-source 5G testbed for AoA estimation, enabling systematic and repeatable experimentation under realistic yet controllable channel conditions. The framework integrates the NVIDIA Sionna RT with a Keysight PROPSIM channel emulator and includes a novel phase calibration procedure for USRP N310 devices. Experimental results show sub-degree to few-degree accuracy, validating the feasibility of lightweight, single-anchor, network-native localization within next-generation 5G systems.

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arXiv2025

Meeting Future Mobile Traffic Needs by Peak-Throughput Design of Next-Gen RAN

Growing congestion in current mobile networks necessitates innovative solutions. This paper explores the potential of mmWave 5G networks in urban settings, focusing on Integrated Access and Backhaul (IAB) and the Smart Radio Environment (SRE). The mmWave traffic will be mainly made of short bursts to transfer large volumes of data and long idle periods where data are processed. This must change the way of designing mobile radio networks. To this extent, we propose network planning models leveraging the maximization of the achievable peak throughput. Results highlight the advantages of this approach during the network planning phase, providing insights into better accommodating the demands of mobile traffic without sacrificing the overall network capacity.

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arXiv2024

Optimal Planning for Heterogeneous Smart Radio Environments

Smart Radio Environment (SRE) is a central paradigms in 6G and beyond, where integrating SRE components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and precise channel models to analyze diverse SRE components, including Reconfigurable Intelligent Surface (RIS), Network-Controlled Repeater (NCR), and advanced technologies like Simultaneous transmitting and reflecting RIS (STAR RIS) and trisectoral NCR (3SNCR). We propose two optimization methods, full coverage minimum cost (FCMC) and maximum budget-constrained coverage (MBCC), that address key cost and coverage objectives by considering both physical characteristics and scalable costs of each component, influenced by factors such as NCR amplification gain and RIS dimensions. Extensive numerical results demonstrate the significant impact of these models in enhancing network planning efficiency for high-density urban environments.

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arXiv2024

Towards Smart Fronthauling Management: Experimental Insights from a 5G Testbed

The fronthaul connection is a key component of Centralized RAN (C-RAN) architectures, consistently required to handle high capacity demands. However, this critical feature is at risk when the transport link relies on wireless technology. Fortunately, solutions exist to enhance the reliability of wireless links. In this paper, we recall the theoretical fronthaul model, present a dynamic reconfiguration strategy and perform a conclusive experiment. Specifically, we showcase the setup of a wireless fronthaul testbed and discuss the resulting measurements. For this task, we leveraged the commercial hardware provided by the High-Frequency Campus Lab (HFCL), a private 5G network with millimeter wave (mmWave) radio access interface. Our experiments provide original data on the fronthaul utilization in this real deployment, demonstrating both a good accordance with the theoretical model discussed in [1] and the viability of one stabilizing solution.

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arXiv2024

Advanced Network Planning in 6G Smart Radio Environments

The growing demand for high-speed, reliable wireless connectivity in 6G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millimeter wave (mmW) bands by enhancing signal reach in environments susceptible to blockage and severe propagation losses. In this paper, we propose an optimized deployment framework aimed at minimizing infrastructure costs while ensuring full area coverage using only RIS and NCR. We formulate a cost-minimization optimization problem that integrates the deployment and configuration of these devices to achieve seamless coverage, particularly in dense urban scenarios. Simulation results confirm that this framework significantly reduces the network planning costs while guaranteeing full coverage, demonstrating RIS and NCR's viability as cost-effective solutions for next-generation network infrastructure.

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arXiv2024

Shaping Radio Access to Match Variable Wireless Fronthaul Quality in Next-Generation Networks

The emergence of Centralized-RAN (C-RAN) has revolutionized mobile network infrastructure, offering streamlined cell-site engineering and enhanced network management capabilities. As C-RAN gains momentum, the focus shifts to optimizing fronthaul links. While fiber fronthaul guarantees performance, wireless alternatives provide cost efficiency and scalability, making them preferable in densely urbanized areas. However, wireless fronthaul often requires expensive over-dimensioning to overcome the challenging atmospheric attenuation typical of high frequencies. We propose a framework designed to continuously align radio access capacity with fronthaul link quality to overcome this rigidity. By gradually adapting radio access capacity to available fronthaul capacity, the framework ensures smooth degradation rather than complete service loss. Various strategies are proposed, considering factors like functional split and beamforming technology and exploring the tradeoff between adaptation strategy complexity and end-to-end system performance. Numerical evaluations using experimental rain attenuation data illustrate the framework's effectiveness in optimizing radio access capacity under realistically variable fronthaul link quality, ultimately proving the importance of adaptive capacity management in maximizing C-RAN efficiency.

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arXiv2024

Mobility-Aware Resource Allocation for mmWave IAB Networks: A Multi-Agent Reinforcement Learning Approach

MmWaves have been envisioned as a promising direction to provide Gbps wireless access. However, they are susceptible to high path losses and blockages, which directional antennas can only partially mitigate. That makes mmWave networks coverage-limited, thus requiring dense deployments. Integrated access and backhaul (IAB) architectures have emerged as a cost-effective solution for network densification. Resource allocation in mmWave IAB networks must face big challenges to cope with heavy temporal dynamics, such as intermittent links caused by user mobility and blockages from moving obstacles. This makes it extremely difficult to find optimal and adaptive solutions. In this article, exploiting the distributed structure of the problem, we propose a Multi-Agent Reinforcement Learning (MARL) framework to optimize user throughput via flow routing and link scheduling in mmWave IAB networks characterized by user mobility and link outages generated by moving obstacles. The proposed approach implicitly captures the environment dynamics, coordinates the interference, and manages the buffer levels of IAB relay nodes. We design different MARL components, considering full-duplex and half-duplex IAB-nodes. In addition, we provide a communication and coordination scheme for RL agents in an online training framework, addressing the feasibility issues of practical systems. Numerical results show the effectiveness of the proposed approach.

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arXiv2024

Exploring Upper-6GHz and mmWave in Real-World 5G Networks: A Direct on-Field Comparison

The spectrum crunch challenge poses a vital threat to the progress of cellular networks and recently prompted the inclusion of millimeter wave (mmWave) and Upper 6GHz (U6G) in the 3GPP standards. These two bands promise to unlock a large portion of untapped spectrum, but the harsh propagation due to the increased carrier frequency might negatively impact the performance of urban Radio Access Network (RAN) deployments. Within the span of a year, two co-located 5G networks operating in these frequency bands were deployed at Politecnico di Milano, Milan, Italy, entirely dedicated to the dense urban performance assessment of the two systems. This paper presents an in-depth analysis of the measurement campaigns conducted on them, with the U6G campaign representing the first of its kind. A benchmark is provided by ray-tracing simulations. The results suggest that networks operating in these frequency bands provide good indoor and outdoor coverage and throughput in urban scenarios, even when deployed in the macro base station setup common to lower frequencies. In addition, a comparative performance analysis of these two key technologies is provided, offering insights on their relative strengths, weaknesses and improvement margins and informing on which bands is better suited for urban macro coverage.

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arXiv2023

Shaping Next-Generation RAN Topologies to Meet Future Traffic Demands: A Peak Throughput Study

Millimeter-Wave (mm-Wave) Radio Access Networks (RANs) are a promising solution to tackle the overcrowding of the sub-6 GHz spectrum, offering wider and underutilized bands. However, they are characterized by inherent technical challenges, such as a limited propagation range and blockage losses caused by obstacles. Integrated Access and Backhaul (IAB) and Reconfigurable Intelligent Surfaces (RIS) are two technologies devised to face these challenges. This work analyzes the optimal network layout of RANs equipped with IAB and RIS in real urban scenarios using MILP formulations to derive practical design guidelines. In particular, it shows how optimizing the peak user throughput of such networks improves the achievable peak throughput, compared to the traditional mean-throughput maximization approaches, without actually sacrificing mean throughputs. In addition, it indicates star-like topologies as the best network layout to achieve the highest peak throughputs.

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arXiv2022

Boosting 5G mm-Wave IAB Reliability with Reconfigurable Intelligent Surfaces

The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges. Network densification has been proposed as a viable solution to increase RAN resilience, and the newly introduced Integrated-Access-and-Backhaul (IAB) is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation and behave as inexpensive passive relays. However, it is not yet clear what role this technology can play in a large RAN deployment. With the scope of filling this gap, we study the blockage resilience of realistic mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been optimised by means of a novel mm-Wave planning tool based on MILP formulation. Numerical results show how adding RISs to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost.

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arXiv2021

Planning Mm-Wave Access Networks With Reconfigurable Intelligent Surfaces

With the capability to support gigabit data rates, millimetre-wave (mm-Wave) communication is unanimously considered a key technology of future cellular networks. However, the harsh propagation at such high frequencies makes these networks quite susceptible to failures due to obstacle blockages. Recently introduced Reconfigurable Intelligent Surfaces (RISs) can enhance the coverage of mm-Wave communications by improving the received signal power and offering an alternative radio path when the direct link is interrupted. While several works have addressed this possibility from a communication standpoint, none of these has yet investigated the impact of RISs on large-scale mm-Wave networks. Aiming to fill this literature gap, we propose a new mathematical formulation of the coverage planning problem that includes RISs. Using well-established planning methods, we have developed a new optimization model where RISs can be installed alongside base stations to assist the communications, creating what we have defined as Smart Radio Connections. Our simulation campaigns show that RISs effectively increase both throughput and coverage of access networks, while further numerical results highlight additional benefits that the simplified scenarios analyzed by previous works could not reveal.

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arXiv2020

Joint Management of Compute and Radio Resources in Mobile Edge Computing: a Market Equilibrium Approach

Edge computing has been recently introduced as a way to bring computational capabilities closer to end users of modern network-based services, in order to support existent and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly manage the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks, as well as the heterogeneity of resources and services' requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources for the execution of their associated end users' jobs, while being constrained by a budget limit. We design an allocation mechanism that employs convex programming in order to find the unique market equilibrium point that maximizes fairness, while making sure that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations in order to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model.

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arXiv2017

Adaptive Robust Traffic Engineering in Software Defined Networks

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.

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arXiv2017

A Multi-traffic Inter-cell Interference Coordination Scheme in Dense Cellular Networks

This paper proposes a novel semi-distributed and practical ICIC scheme based on the Almost Blank SubFrame (ABSF) approach specified by 3GPP. We define two mathematical programming problems for the cases of guaranteed and best-effort traffic, and use game theory to study the properties of the derived ICIC distributed schemes, which are compared in detail against unaffordable centralized schemes. Based on the analysis of the proposed models, we define Distributed Multi-traffic Scheduling (DMS), a unified distributed framework for adaptive interference-aware scheduling of base stations in future cellular networks which accounts for both guaranteed and best-effort traffic. DMS follows a two-tier approach, consisting of local ABSF schedulers, which perform the resource distribution between guaranteed and best effort traffic, and a lightweight local supervisor, which coordinates ABSF local decisions. As a result of such a two-tier design, DMS requires very light signaling to drive the local schedulers to globally efficient operating points. As shown by means of numerical results, DMS allows to (i) maximize radio resources reuse, (ii) provide requested quality for guaranteed traffic, (iii) minimize the time dedicated to guaranteed traffic to leave room for best-effort traffic, and (iv) maximize resource utilization efficiency for best-effort traffic.

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