The freshness of sensor data is critical for all types of cyber-physical systems. An established measure for quantifying data freshness is the Age-of-Information (AoI), which has been the subject of extensive research. Recently, there has been increased interest in multi-sensor systems: redundant sensors producing samples of the same physical process, sensors such as cameras producing overlapping views, or distributed sensors producing correlated samples. When the information from a particular sensor is outdated, fresh samples from other correlated sensors can be helpful. To quantify the utility of distant but correlated samples, we put forth a two-dimensional (2D) model of AoI that takes into account the sensor distance in an age-equivalent representation. Since we define 2D-AoI as equivalent to AoI, it can be readily linked to existing AoI research, especially on parallel systems. We consider physical phenomena modeled as spatio-temporal processes and derive the 2D-AoI for different Gaussian covariance kernels. For a basic exponential product kernel, we find that spatial distance causes an additive offset of the AoI, while for other kernels the effects of spatial distance are more complex and vary with time. Using our methodology, we evaluate the 2D-AoI of different spatial topologies and sensor densities.

This document presents a comprehensive validation and integration methodology designed to ensure the realization of the MultiX technologies using vertical oriented Proof of Concepts (PoCs). The document provides an overall description of the methodology to be used during the project execution, focusing on the roadmap and phases envisioned during the project, and the mechanisms established to monitor the progress of the integration and validation activities, and at the same time assess the completeness of the targeted tests. This document also describes the MultiX Open Labs environments, detailing the available and targeted technologies to be used during the validation activities, and the mechanisms to access the testbeds to deploy software components. Finally, this document provides an initial set of experiments and demonstrations envisioned during the project execution providing details regarding the MultiX sensing technologies to be validated and the targeted KPIs.

Integrated sensing and communication (ISAC) has gained significant traction in recent years, primarily because it allows existing communication infrastructure to support sensing applications with minimal additional costs. In particular, millimeter-wave (mmWave) ISAC has the potential to offer improved sensing performance in applications such as pose estimation and gesture recognition. For complex sensing tasks and environments, data-driven sensing, which relies on deep learning, is becoming increasingly popular and has shown promising results. However, deep learning models for these tasks require large labeled datasets to achieve high accuracy. Dataset collection and labeling are labor-intensive and time-consuming. Consequently, there is growing interest in leveraging unlabeled data to overcome these challenges. To address this, we propose mmGAN, a semi-supervised method for ISAC-based gesture recognition. We propose a novel loss function for mmGAN based on softplus, feature matching, and manifold regularization to significantly improve gesture recognition performance. We evaluate mmGAN on a 5G Orthogonal Frequency Division Multiplexing (OFDM) mmWave dataset comprising power per beam pair measurements. When training both mmGAN and the supervised baseline with only 0.6% of the labeled data, mmGAN demonstrates up to 25 percentage points higher accuracy than the supervised baseline. Our method serves as a strong foundation for cross-subject transfer learning, demonstrating the significant value of leveraging unlabeled data to enhance cross-domain sensing performance in ISAC systems. Our results demonstrate that the proposed loss function achieves superior performance across diverse subjects. Further, mmGAN significantly narrows the performance gap between semi-supervised and fully supervised models on the publicly available Widar dataset. Moreover, we provide an interpretable analysis of mmGAN performance through saliency maps and ablation studies, revealing key insights into the model’s behavior and generalization. This work is the first to evaluate gesture recognition performance in 5G OFDM mmWave ISAC systems using a semi-supervised learning approach, covering the entire pipeline from testbed implementation to model evaluation.

Recently, the availability of low-cost commercial-off-the-shelf (COTS) millimeter-wave (mmWave) radars has accelerated their widespread adoption across diverse applications. Their compact size and high range resolution make them suitable as onboard sensors for autonomous vehicles, robots, and drones. However, their limited number of antennas constrains the angular resolution, degrading localization performance in cluttered environments. Moreover, dense deployment of on-device mmWave radars often suffers from non-negligible mutual interference, leading to ghost targets and measurement artifacts. While interference is typically regarded as a challenge, InterAngle leverages the temporal and spatial sparsity of line-of-sight (LoS) interference to enable accurate inter-device angle of arrival (AoA) estimation, even at sub-resolution angular separations and in the presence of strong environmental reflections. By addressing the practical challenges of generating, detecting, and exploiting interference signals, InterAngle unlocks new sensing opportunities beyond the AoA estimation. Our method reduces the target radar’s AoA estimation error from 7.71° to 1.31° at sub-resolution angular separations, and from 12.16° to 1.00° with strong surrounding reflections.

This article presents mmHSense, a set of six labeled mmWave datasets to support human sensing research within Integrated Sensing and Communication (ISAC) systems. The datasets can be used to explore mmWave ISAC for various end applications such as gesture recognition, person identification, pose estimation, and localization. Moreover, the datasets can be used to develop and advance signal processing and deep learning research on mmWave ISAC. This article describes the testbed, experimental settings, and signal features for each dataset. The datasets cover both Wi-Fi and 5G signals. Furthermore, the utility of the datasets is demonstrated through validation on different downstream tasks such as pose estimation, gesture recognition, localization, and person identification. In addition, we demonstrate the use of parameter-efficient fine-tuning to adapt ISAC models to different tasks, significantly reducing computational complexity while maintaining performance on prior tasks.

As we approach higher and higher localization and sensing accuracies with 6G technologies, the radio capabilities and signal processing will cease to be the main accuracy limitations, and other physical phenomena start to play an increasingly critical role. In this paper, we investigate how radio-based physical measurements, including radar, wireless localization and Integrated Sensing and Communication (ISAC), can be affected by environmental conditions, and at what level of accuracy and dimensional and temporal scales these effects become significant. Specifically, we produce and publish a dataset where the long-term stability of radio-based range measurements is empirically assessed, showing peak-to-peak variations of 0.3 mm over a 1 m range (which scales linearly, e.g. 3 mm over 10 m or 3 cm over 100 m) over 6 months. Further, we provide and substantiate a mechanistic explanation of these variations by showing how the combination of changes in refractive index of the air and thermal expansion of the room matches the measured data. We further demonstrate how such effects can be compensated for by measuring the atmospheric conditions and exploiting a combination of physical modeling and machine learning.

Unique-word orthogonal frequency division multi-plexing (UW-OFDM) addresses the spectral inefficiency of cyclic prefix (CP) OFDM by embedding the guard interval within the symbol period. However, its practical adoption is hindered by a prohibitive computational bottleneck. Conventional methods for embedding unique word within the DFT window rely on complex matrix inversions at the transmitter (Tx). While alternative zero-padded (ZP) schemes avoid this complexity, they place guard interval outside DFT window, breaking natural circularity and necessitating noise-enhancing Overlap-Add receiver (Rx). This letter proposes a scalable, closed-form isometric Spectral Precoding architecture that resolves this trade-off. By exploiting frequency-domain interpolation, we implicitly construct the required near-zero-energy tail within the DFT window using only deterministic linear transformations. We prove this construction reduces Tx complexity to log-linear order while preserving standard single-tap equalization without Rx noise enhancement. Validated with 5G-NR numerologies, the scheme matches CP-OFDM error performance while eliminating CP overhead.

Forecasting time series over long horizons is essential for proactive decision-making in many systems. Recent research has focused on transformer-based architectures, which capture long-range dependencies in sequential data. However, several studies show that simpler linear models can outperform transformers by avoiding overfitting during training. In this context, we present NeuroFlexMLP, a deep learning model for multivariate time series forecasting tasks. NeuroFlexMLP's key distinct feature is the adaptability to the diverse complexity of real-world time series, which is achieved, from the architecture standpoint, by adding non-linear residual blocks to a first linear block. This architectural design simplifies hyperparameter optimization, leading to accurate forecasts for various time series data types regardless of the lookback or prediction horizons, outperforming state-of-the-art (SOTA) models on challenging real-world datasets. Its Multi-Layer Perceptron (MLP) design ensures high computational efficiency, making it scalable for longer input sequences than transformer-based models. We validate NeuroFlexMLP for the LEO satellite beam hopping use case, where its lightweight design enables on-board deployment, and on state-of-the art AI datasets. Across all these benchmarks, NeuroFlexMLP achieves competitive accuracy over state-of-the-art models while providing an adaptive architecture that significantly reduces computational overhead. On the LEO beam hopping task, it achieves up to 35.9% MSE reduction over Informer, which translates into up to 28% lower provisioning cost under asymmetric cost models that penalize under-allocation more heavily than over-allocation.

Near-field localization for ISAC requires large-aperture arrays, making fully-digital implementations prohibitively complex and costly. While sparse subarray architectures can reduce cost, they introduce severe estimation ambiguity from grating lobes. To address both issues, we propose SHARE (Sparse Hierarchical Angle-Range Estimation), a novel two-stage sparse recovery algorithm. SHARE operates in two stages. It first performs coarse, unambiguous angle estimation using individual subarrays to resolve the grating lobe ambiguity. It then leverages the full sparse aperture to perform a localized joint angle-range search. This hierarchical approach avoids an exhaustive and computationally intensive two-dimensional grid search while preserving the high resolution of the large aperture. Simulation results show that SHARE significantly outperforms conventional one-shot sparse recovery methods, such as Orthogonal Matching Pursuit (OMP), in both localization accuracy and robustness. Furthermore, we show that SHARE's overall localization accuracy is comparable to or even surpasses that of the fully-digital 2D-MUSIC algorithm, despite MUSIC having access to the complete, uncompressed data from every antenna element. SHARE therefore provides a practical path for high-resolution near-field ISAC systems.

This talk discusses the transition from standard signal processing to advanced phase-coherent wireless sensing. We first present practical algorithms for carrier-phase-based localization that can achieve millimeter-level precision. While high-accuracy localization is an important capability in its own right for 6G applications, it also serves as the essential prerequisite for advanced wireless imaging. To this end, we address the challenges of over-the-air synchronization required to correct time and frequency offsets in distributed multistatic systems. Finally, we discuss methods for high-accuracy coherent imaging of moving objects, that allow multiple receivers to cooperate as a large-scale synthetic aperture. The talk highlights experimental results and real-world demonstrations, showing a practical path toward turning communication networks into high-resolution sensing platforms.

Localization is a key feature of future Sixth Generation (6G) net-works with foreseen accuracy requirements down to the millimeter level, to enable novel applications in the fields of telesurgery, high-precision manufacturing, and others. Currently, such accuracy requirements are only achievable with specialized or highly resource-demanding systems, rendering them impractical for more wide-spread deployment. In this paper, we present the first system that enables low-complexity and low-bandwidth absolute 3D localization with millimeter-level accuracy in generic wireless networks. It performs a carrier phase-based wireless localization refinement of an initial location estimate based on successive location-likelihood optimization across multiple bands. Unlike previous phase unwrapping methods, our solution is one-shot. We evaluate its performance collecting ~350, 000 measurements, showing an improvement of more than one order of magnitude over classical localization techniques. Finally, we will open-source the low-cost, modular FR3 front-end that we developed for the experimental campaign.

Integrated Sensing And Communication (ISAC) is recognized as a key enabler for future 6th Generation (6G) networks, combining communication capabilities with pervasive sensing. In such systems, the estimation of the Doppler shift plays a crucial role for target characterization. However, typical real-world ISAC scenarios largely involve bistatic or multistatic configurations and mobile ISAC nodes. Under these conditions, Doppler estimation becomes particularly challenging, as clock asynchrony between the Transmitter (TX) and the Receivers (RXs), combined with their mobility, introduces additional Doppler components and phase offsets that distort or disrupt the target-induced frequency shift. Existing works have considered these challenges separately or relied on external reference reflectors. In this paper, we present the first method to estimate the Doppler frequency of a target with mobile and asynchronous ISAC nodes in a multistatic configuration, considering the case of a mobile TX and multiple static RXs, and without leveraging any external reflector. By leveraging the invariance of the phase offsets across multipath components and exploiting geometrical relationships, we show that the problem is solvable if at least 4 RXs are present. We evaluate the proposed solution through numerical simulations in various scenarios, showing that it is a valid approach for estimating target Doppler shifts in unsynchronized multistatic ISAC deployments with mobile nodes.

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 Communication (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.

Radio Access Network (RAN) sharing, which often also includes spectrum sharing, is a strategic cooperative agreement among two or more mobile operators, where one operator may use another's RAN infrastructure to provide mobile services to its users. By mutually sharing physical sites, radio elements, licensed spectrum and other parts of the RAN infrastructure, participating operators can significantly reduce the capital (and operational) expenditure in deploying and operating cellular networks, while accelerating coverage expansion – thereby addressing the spectrum scarcity and infrastructure cost challenges in the 5G era and beyond. While the economic benefits of RAN sharing are well understood, the impact of such resource pooling on user-perceived performance remains underexplored, especially in real-world commercial deployments. We present, to the best of our knowledge, the first empirical measurement study of commercial 5G spectrum and RAN sharing. Our measurement study is unique in that, beyond identifying real-world instances of shared 5G spectrum and RAN deployment "in the wild", we also analyze users' perceived performance and its implication on Quality of Experience (QoE). Our study provides critical insights into resource management (i.e., pooling) and spectrum efficiency, offering a blueprint (and implications) for network evolution in 5G, 6G and beyond.

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.

Interpreting time series predictions from advanced Machine Learning and Deep Learning (ML/DL) models is challenging, as these models often function as black boxes, limiting their applicability in critical domains. To address this, we leverage CHRONOPROF, an Explainable AI (XAI) technique specifically designed for time series data, built upon the SHAP framework. CHRONOPROF improves interpretability by deriving virtual weights from SHAP values, offering a linearized representation of complex model decisions while preserving temporal coherence. However, CHRONOPROF’s complexity can pose challenges for non-expert users. To mitigate this, we developed an interactive dashboard that simplifies interpretation by retrieving stored data and SHAP values to compute and visualize virtual weights along with other representations that are derived from them. This user-friendly interface enables users to explore model behavior across different models and datasets. Ultimately, this innovation facilitates the adoption of CHRONOPROF and fosters trust in AI-driven network operations.

This article presents DISC, a dataset of millimeter- wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response, including scattered signals off the moving body parts of subjects moving in an indoor environment. The provided data consists of three parts for more than two hours of channel measurements with high temporal resolution (0.27 ms inter-packet time). DISC contains the contribution of seven subjects performing five different activities and includes data collected from two distinct environments. Unlike available radar-based millimeter-wave sensing datasets, our measurements are collected using uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. We develop, train, and release open-source baseline algorithms based on DISC to perform human sensing tasks. Our results demonstrate that DISC can serve as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communications.

The operation of future 6th-generation (6G) mobile networks will increasingly rely on the ability of Deep Reinforcement Learning (DRL) to optimize network decisions in real-time. DRL yields demonstrated efficacy in various resource allocation problems, such as joint decisions on user scheduling and antenna allocation or simultaneous control of computing resources and modulation. However, trained DRL agents are closed-boxes and inherently difficult to explain, which hinders their adoption in production settings. In this paper, we make a step towards removing this critical barrier by presenting SYMBXRL, a novel technique for EXplainable Reinforcement Learning (XRL) that synthesizes human-interpretable explanations for DRL agents. SYMBXRL leverages symbolic AI to produce explanations where key concepts and their relationships are described via intuitive symbols and rules; coupling such a representation with logical reasoning exposes the decision process of DRL agents and offers more comprehensible descriptions of their behaviors compared to existing approaches. We validate SYMBXRL in practical network management use cases supported by DRL, proving that it not only improves the semantics of the explanations but also paves the way for explicit agent control: for instance, it enables intent-based programmatic action steering that improves by 12% the median cumulative reward over a pure DRL solution.