arXiv subjects

Dario Tagliaferri

Publications and source records attributed to Dario Tagliaferri.

At least 19 recordsLinked to original sources

arXiv2026

Radio Imaging and Resource Allocation in Frugal Multistatic D-MIMO ISAC Systems

Emerging integrated sensing and communication (ISAC) systems based on distributed MIMO (D-MIMO) enable radio imaging by exploiting spatial diversity across multiple access points (APs). However, joint sensing and communication introduce mutual interference between communication and sensing signals. In this paper, we propose a downlink D-MIMO ISAC framework that allocates orthogonal subcarriers to sensing and communication to eliminate inter-function interference. We consider a phase-coherent architecture in which single-antenna APs serve communication user equipment (UEs) while constructing a reflectivity image of the environment. We develop a two-timescale resource allocation framework that minimizes reconstructed-image entropy subject to a communication spectral-efficiency (SE) constraint. The proposed design follows a communication-centric policy, where imaging uses the resources left after satisfying the communication requirement. The long-timescale optimization (LTO) determines AP modes and subcarrier assignment, including the partitioning between communication and sensing and the allocation of sensing subcarriers among transmit APs, using synthetic scenarios with random UE and target distributions. The short-timescale optimization (STO) adapts the communication-sensing power-splitting factor to preserve the SE constraint in the current scenario. Numerical results show that the proposed orthogonal subcarrier allocation achieves a superior sensing-communication trade-off compared with conventional superposition, where sensing and communication share the same subcarriers. Finally, we evaluate coherent and non-coherent imaging receivers and identify the synchronization regimes in which each approach provides better imaging and localization performance.

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arXiv2026

Near-Field Velocity Estimation and Doppler-Aware Localization in OFDM Massive MIMO

In Orthogonal Frequency Division Multiplexing (OFDM)-based massive Multiple-Input Multiple-Output (MIMO) near-field (NF) sensing, target motion induces an antenna-dependent bistatic Doppler variation across the array aperture. Ignoring this spatial Doppler variation leads to a model mismatch that degrades NF localization. In this paper, we propose a low-complexity recursive framework for joint radial/transverse velocity estimation and Doppler-aware localization. Initialized by a constant-Doppler coarse localization, the method alternates between closed-form Least Squares Estimator (LSE)-based velocity estimation and antenna-dependent Doppler-aware localization refinement. Simulation and measurement results demonstrate the effectiveness of the proposed framework against two benchmark methods. Compared with a low-complexity constant-Doppler baseline method, the proposed algorithm improves range, angle, and radial velocity estimation results, while also enabling transverse velocity estimation. In the measurement results, the overall localization error decreases from 0.268 m to 0.064 m. The radial and transverse velocity estimation errors are 0.032 m/s and 0.069 m/s, respectively. Compared with a high-complexity exhaustive four-dimensional (4D) Maximum Likelihood Estimator (MLE), the proposed method achieves comparable velocity estimation results while yielding a more accurate localization result when the 4D MLE has a practical finite search grid.

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arXiv2026

Beyond Point Targets: Experimental Analysis of Frequency Anisotropy for Multi-band ISAC in FR3

As Integrated Sensing And Communication (ISAC) systems push toward higher sensing resolution, multi-band processing has emerged as a key enabler, with Frequency Range 3 (FR3) (7-24 GHz) standing out for its combination of wide bandwidth and favorable propagation. A common assumption underlying existing multi-band ISAC techniques is that targets behave as frequency-invariant point scatterers, enabling coherent combination of measurements across widely spaced subbands. However, this assumption does not hold over wide fractional bandwidths, since real objects exhibit frequency-dependent scattering mechanisms and migrating scattering centers. This paper provides the first systematic experimental characterization of the frequency anisotropy of everyday objects for ISAC, using channel measurements collected with a calibrated vector network analyzer over the 6-24 GHz band, across 10 objects and 120 viewpoints. We process bistatic channel impulse responses and synthetic aperture radar images to quantify multi-band coherence, extracting the real part of the cross-band correlation coefficient. Our results reveal complex, non-trivial coherence structures that vary substantially with object type and viewing angle, highlighting the need to account for frequency anisotropy in multi-band ISAC system design.

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arXiv2026

Hierarchical Coherent Imaging of Composite Anisotropic Moving Targets in ISAC

In Integrated Sensing and Communication (ISAC) networks, distributed devices can cooperate to produce radio images of the surrounding environment by exploiting phase-coherent signal processing. However, existing imaging methods are not well-suited for composite moving targets with multiple independently moving extended parts. This is due to simplistic isotropic scattering models and the lack of methods to compensate for distinct Doppler shifts from each component, which leads to image defocusing. We propose MOSAIC, the first hierarchical imaging method for composite moving targets using distributed User Equipments (UEs) and a single ISAC Base Station (BS). MOSAIC generates high-resolution images of each target part and estimates its velocity vector. Coherent imaging is performed within selected clusters of UEs observing a locally isotropic scattering from each part, while cluster-specific images are combined non-coherently across wide angles to improve the reconstruction. To mitigate Doppler-induced defocusing, Doppler components are pre-compensated before coherent imaging, turning a limitation into an additional means of resolving multiple target parts. This also enables low-complexity velocity estimation by associating Doppler frequencies across UEs. Simulations show over 50% improvement in image quality compared to existing methods, in terms of Wasserstein distance, and dm/s-level velocity estimation accuracy.

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arXiv2026

Mobile Radio Networks and Weather Radars Dualism: Rainfall Measurement Revolution in Densely Populated Areas

This study demonstrates, for the first time, how a network of cellular base stations (BSs) - the infrastructure of mobile radio networks - can be used as a distributed opportunistic radar for rainfall remote sensing. By adapting signal-processing techniques traditionally employed in Doppler weather radar systems, we demonstrate that BS signals can be used to retrieve typical weather radar products, including reflectivity factor, mean Doppler velocity, and spectral width. Due to the high spatial density of BS infrastructure in urban environments, combined with intrinsic technical features such as electronically steerable antenna arrays and wide receiver bandwidths, the proposed approach achieves unprecedented spatial and temporal resolutions, on the order of a few meters and several tens of seconds, respectively. Despite limitations related to low transmitted power, limited antenna gain, and other system constraints, a major challenge arises from ground clutter contamination, which is exacerbated by the nearly horizontal orientation of BS antenna beams. This work provides a thorough assessment of clutter impact and demonstrates that, through appropriate processing, the resulting clutter-filtered radar moments reach a satisfactory level of quality when compared with raw observations and with measurements from independent BSs with overlapped field-of-views. The findings highlight a transformative opportunity for urban hydrometeorology: leveraging existing telecommunications infrastructure to obtain rainfall information with a level of spatial granularity and temporal immediacy like never before.

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arXiv2026

Beyond Legacy OFDM: A Mobility-Adaptive Multi-Gear Framework for 6G

While Third Generation Partnership Project (3GPP) has confirmed orthogonal frequency division multiplexing (OFDM) as the baseline waveform for sixth-generation (6G), its performance is severely compromised in the high-mobility scenarios envisioned for 6G. Building upon the GEARBOX-PHY vision, we present gear-switching OFDM (GS-OFDM): a unified framework in which the base station (BS) adaptively selects among three gears, ranging from legacy OFDM to delay-Doppler domain processing based on the channel mobility conditions experienced by the user equipments (UEs). We illustrate the benefit of adaptive gear switching for communication throughput and, finally, we conclude with an outlook on research challenges and opportunities.

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arXiv2026

NLOS-Aided Joint OTA Synchronization and Off-Grid Imaging for Distributed MIMO Systems

Distributed multiple-input multiple-output (MIMO) architectures enable large-scale integrated sensing and communication (ISAC) by providing high spatial resolution and robustness through spatial diversity. However, practical phase-coherent sensing is challenged by phase synchronization errors and modeling mismatch caused by grid discretization. Existing over-the-air (OTA) synchronization methods typically treat synchronization and sensing tasks separately, which may lead to inaccurate phase alignment when multipath components are used for imaging. In this paper, we propose a non-line-of-sight (NLOS)-aided joint OTA synchronization and off-grid imaging framework for distributed MIMO ISAC systems. First, a line-of-sight (LOS)-assisted coarse synchronization is performed to establish initial phase coherence across distributed links. Subsequently, an iterative refinement stage exploits reconstructed NLOS components obtained from imaging results. By modeling off-grid effects via a first-order Taylor expansion, we transform measurements with nonlinear off-grid offset into an augmented linear model with jointly sparse reflectivity and off-set variables. The imaging problem is reformulated as a structured sparse recovery task and solved using a tailored off-grid approximate message passing (OG-AMP) algorithm. The imaging and synchronization modules are coupled within a closed-loop alternative optimization framework, where improved imaging enables more accurate phase refinement, and vice versa. Numerical results show that the proposed framework achieves accurate synchronization and imaging under phase errors. Compared with conventional approaches, it shows superior robustness and accuracy.

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arXiv2025

POLO: Phase-Only Localization in Uplink Distributed MIMO Systems

We propose a low-complexity localization framework for uplink distributed MIMO (D-MIMO) systems, targeting the challenge of minimizing the highly spiky maximum-likelihood (ML) cost function that arises in sparsely deployed phasecoherent access points (APs) with narrowband transmission. In such systems, ML-based localization typically relies on dense grid search, incurring prohibitive computational complexity. To address this, we introduce phase-only localization (POLO), an approach that leverages differential carrier-phase measurements from selected APs to generate a compact set of candidate user positions. The ML cost function is then evaluated only at these candidates, reducing complexity significantly. A key challenge is to devise an AP selection mechanism that reduces the number of candidate points while maintaining reliable coverage. We propose two variants: POLO-I, which selects three APs to provide closed-form candidate positions with low computational cost, and POLO-II, which selects four APs using an alternative strategy that enhances coverage at marginally higher runtime. Comprehensive analytical and simulation results show that POLO achieves a favorable coverage-complexity trade-off, reducing cost by orders of magnitude relative to exhaustive grid search with only marginal loss in coverage. By characterizing this tradeoff under diverse AP configurations, we also provide practical guidelines for selecting between POLO-I and POLO-II depending on latency and coverage requirements.

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arXiv2025

Enabling NLOS Imaging Capabilities at the Initial Access of 6G Base Stations

Sensing in non-line-of-sight (NLOS) is one of the major challenges for integrated sensing and communication systems. Existing countermeasures for NLOS either use prior knowledge on the environment to characterize all the multiple bounces or deploy anomalous reflectors in the environment to enable communication infrastructure to ''\textit{see behind the corner}''. This work addresses the integration of monostatic NLOS imaging functionalities into the initial access (IA) procedure of a next generation base station (BS), by means of a non-reconfigurable modular reflector. During standard-compliant IA, the BS sweeps a narrow beam using a pre-defined dedicated codebook to achieve the beam alignment with users. We introduce the imaging functionality by enhancing such codebook with imaging-specific entries that are jointly designed with the angular configuration of the modular reflector to enable high-resolution imaging of a region in NLOS by \textit{coherently} processing all the echoes at the BS. We derive closed-form expressions for the near-field (NF) spatial resolution, as well as for the \textit{effective aperture} (i.e., the portion of the reflector that actively contributes to improve image resolution). The problem of imaging of moving targets in NLOS is also addressed, and we propose a maximum-likelihood estimation for target's velocity in NF and related theoretical bound. Further, we discuss and quantify the inherent communication-imaging performance trade-offs and related system design challenges through numerical simulations. Finally, the proposed imaging method employing modular reflectors is validated both numerically and experimentally, showing the effectiveness of our concept.

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arXiv2025

Integrating Phase-Coherent Multistatic Imaging in Downlink D-MIMO Networks

This paper addresses the challenge of integrating multistatic coherent imaging functionalities in the downlink (DL) of a phase-coherent distributed multiple input multiple output (D-MIMO) communication network. During DL, the D-MIMO access points (APs) jointly precode the transmitted signals to maximize the spectral efficiency (SE) at the users (UEs) locations. However, imaging requires that \textit{(i)} a fraction of the APs work as receivers for sensing and \textit{(ii)} the transmitting APs emit AP-specific and orthogonal signals to illuminate the area to be imaged and allow multistatic operation. In these settings, our contribution is twofold. We propose a novel distributed integrated sensing and communication (D-ISAC) system that superposes a purposely designed AP-specific signal for imaging to the legacy UE-specific communication one, with a tunable trade-off factor. We detail both the imaging waveform design according to the \textit{extended orthogonality condition} and the space-frequency precoder design. Then, we propose an optimized selection strategy for the receiving APs, in order to maximize imaging performance under half-duplex constraints. Extensive numerical results prove the feasibility and benefits of our proposal, materializing the potential of joint multistatic imaging and communications in practical D-MIMO deployments.

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arXiv2025

Toward Multiband Sensing in FR3: Frequency Anisotropy Characterization and Non-Contiguous Bands Aggregation Algorithms

Frequency Range 3 (FR3) in the 7-24 GHz band will be the new spectrum for 6G wireless networks. The bandwidth availability and diversity of FR3 offer unprecedented opportunities for coherent multiband Integrated Sensing and Communications (ISAC), which aggregates the carrier phase information from multiple frequency bands to increase the sensing resolution to the cm-level. However, the frequency anisotropy of sensing targets over GHz-wide bands and the non-contiguity of the 6G spectrum, pose critical challenges to the application of existing multiband ISAC techniques. We present the first study on coherent multiband sensing in FR3. We experimentally characterize the frequency anisotropy of targets and propose new phase coherence metrics for multiband processing. Then, we analyze the impact of non-contiguous FR3 bands considered by 3GPP, and design a new algorithm to mitigate the resulting sensing artifacts, outperforming existing techniques. Our results represent a first step toward fully developing multiband ISAC for FR3.

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arXiv2025

Bridging the Gap via Data-Aided Sensing: Can Bistatic ISAC Converge to Genie Performance?

We investigate data-aided iterative sensing in bistatic OFDM ISAC systems, focusing on scenarios with co-located sensing and communication receivers. To enhance target detection beyond pilot-only sensing methods, we propose a multi-stage bistatic OFDM receiver, performing iterative sensing and data demodulation to progressively refine ISAC channel and data estimates. Simulation results demonstrate that the proposed data-aided scheme significantly outperforms pilot-only benchmarks, particularly in multi-target scenarios, substantially narrowing the performance gap compared to a genie-aided system with perfect data knowledge. Moreover, the proposed approach considerably expands the bistatic ISAC trade-off region, closely approaching the probability of detection-achievable rate boundary established by its genie-aided counterpart.

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arXiv2025

Joint Localization and Synchronization in Downlink Distributed MIMO

We investigate joint localization and synchronization in the downlink of a distributed multiple-input-multiple-output (D-MIMO) system, aiming to estimate the position and phase offset of a single-antenna user equipment (UE) using downlink transmissions of multiple phase-synchronized, multi-antenna access points (APs). We propose two transmission protocols: sequential (P1) and simultaneous (P2) AP transmissions, together with the ML estimators that either leverage (coherent estimator) or disregard phase information (non-coherent estimator). Simulation results reveal that downlink D-MIMO holds significant potential for high-accuracy localization while showing that P2 provides superior localization performance and reduced transmission latency.

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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

Multi-View Integrated Imaging and Communication

Non-line-of-sight (NLOS) operation is one of the open issues to be solved for integrated sensing and communication (ISAC) systems to become a pillar of the future wireless infrastructure above 10 GHz. Existing NLOS countermeasures use either metallic mirrors, that are limited in coverage, or reconfigurable metasurfaces, that are limited in size due to cost. This paper focuses on integrated imaging and communication (IIAC) systems for NLOS exploration, where a base station (BS) serves the users while gathering a high-resolution image of the area. We exploit a large reflection plane, that is phase-configured in space and time jointly with a proper BS beam sweeping to provide a multi-view observation of the area and maximizing the image resolution. Remarkably, we achieve a near-field imaging through successive far-field acquisitions, limiting the design complexity and cost. Numerical results prove the benefits of our proposal.

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arXiv2024

Deep Learning-based Target-To-User Association in Integrated Sensing and Communication Systems

In Integrated Sensing and Communication (ISAC) systems, matching the radar targets with communication user equipments (UEs) is functional to several communication tasks, such as proactive handover and beam prediction. In this paper, we consider a radar-assisted communication system where a base station (BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a double aim: (i) associate vehicular radar targets to vehicular equipments (VEs) in the communication beamspace and (ii) predict the beamforming vector for each VE from radar data. The proposed target-to-user (T2U) association consists of two stages. First, vehicular radar targets are detected from range-angle images, and, for each, a beamforming vector is estimated. Then, the inferred per-target beamforming vectors are matched with the ones utilized at the BS for communication to perform target-to-user (T2U) association. Joint multi-target detection and beam inference is obtained by modifying the you only look once (YOLO) model, which is trained over simulated range-angle radar images. Simulation results over different urban vehicular mobility scenarios show that the proposed T2U method provides a probability of correct association that increases with the size of the BS antenna array, highlighting the respective increase of the separability of the VEs in the beamspace. Moreover, we show that the modified YOLO architecture can effectively perform both beam prediction and radar target detection, with similar performance in mean average precision on the latter over different antenna array sizes.

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arXiv2024

Sensing in NLOS: a Stroboscopic Approach

Sensing in non-line-of-sight (NLOS) is a well-known issue that limits the effective range of radar-like sensors. Existing approaches for NLOS sensing consider the usage of either metallic mirrors, that only work under specular reflection, or dynamically-reconfigurable metasurfaces that steer the signal to cover a desired area in NLOS, with the drawback of cost and control signaling. This paper proposes a novel sensing system, that allows a source to image a desired region of interest (ROI) in NLOS, using the combination of a proper beam sweeping (by the source) as well as a passive reflection plane configured as a periodic angular deflecting function (that allows illuminating the ROI). \textit{Stroboscopic sensing} is obtained by sweeping over a sufficiently large portion of the reflection plane, the source covers the ROI \textit{and} enhance the spatial resolution of the image, thanks to multiple diverse observation angles of ROI. Remarkably, the proposed system achieves a near-field imaging with a sequence of far-field acquisitions, thus limiting the implementation complexity. We detail the system design criteria and trade-offs, demonstrating the remarkable benefits of such a stroboscopic sensing system, where a possibly moving source can observe a ROI through multiple points of view as if it were static.

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arXiv2024

Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy

The sixth generation (6G) of wireless networks introduces integrated sensing and communication (ISAC), a technology in which communication and sensing functionalities are inextricably linked, sharing resources across time, frequency, space, and energy. Despite its popularity in communication, the orthogonal frequency division multiplexing (OFDM) waveform, while advantageous for communication, has limitations in sensing performance within an ISAC network. This paper delves into OFDM waveform design through optimal resource allocation over time, frequency, and energy, maximizing sensing performance while preserving communication quality. During quasi-normal operation, the Base Station (BS) does not utilize all available time-frequency resources, resulting in high sidelobes in the OFDM waveform's ambiguity function, as well as decreased sensing accuracy. To address these latter issues, the paper proposes a novel interpolation technique using matrix completion through the Schatten p quasi-normal approximation, which requires fewer samples than the traditional nuclear norm for effective matrix completion and interpolation. This approach effectively suppresses the sidelobes, enhancing the sensing performance. Numerical simulations confirm that the proposed method outperforms state-of-the-art frameworks, such as standard complaint resource scheduling and interpolation, particularly in scenarios with limited resource occupancy.

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