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Giuseppe Caire

Publications and source records attributed to Giuseppe Caire.

At least 37 records · Page 2Linked to original sources
arXiv2026

ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics

Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Language-Action (VLA) models that enable this are too large to run on the robot; a common trend is to offload inference to the cloud. The wireless link, however, limits how much sensing data the edge can transmit per control step. Two recent lines address this constraint: semantic communication codecs compress sensor data but require channel-specific retraining, and VLA token pruners select tokens from image but ignore the channel. Our insight is that the dense semantic information contained in the language already indicates which visual tokens matter. We propose ComVLA, a framework that uses this language guidance to adapt the VLA token budget to the channel capacity. Transmitting 32 tokens instead of 512 on the LIBERO benchmark, ComVLA cuts inference compute by 74% and inference latency by 22% versus the original OpenVLA-OFT baseline, at a cost of 1.5 pp in average task success (95.4% vs. 96.9%), and it stays within the capacity budget under Rayleigh and Rician fading. These results demonstrate that co-designing VLA inference and wireless communication is a practical direction for 6G-connected robotics.

cs.RO↗
arXiv2026

Foundation Models for Generalizable Semantic and Goal-Oriented Communication

Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate budgets. Many existing systems overfit their training data and degrade sharply at very low bit rates because they attempt to compress the entire signal. We introduce Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual-linguistic Foundation Model priors to mitigate overfitting. It further improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the masked regions. By decoupling what to send from how to reconstruct, a vision-language foundation model selects and transmits a sparse set of semantic anchors, while a pretrained diffusion model, fine-tuned for masked completion, reconstructs the image at the receiver. In our experiments, FMSGOC reaches 0.039 bits per pixel (BPP), maintains high semantic fidelity (cosine similarity 0.87-0.90 on CIFAR-10), remains robust on previously unseen inputs (0.83-0.86 on ImageNet), and shows good perceptual similarity (0.1278/0.1558, CIFAR-10/ImageNet), outperforming strong end-to-end baselines at lower bit rates.

cs.LG↗
arXiv2026

Spectral-Efficient MIMO-OFDM: Low-Complexity Solution based on Random Multiplexing

This paper presents a low-complexity precoded MIMO-OFDM system for achieving improved spectral efficiency (SE) via intentionally compressing information symbols among subcarriers. Particularly, the proposed scheme leverages the powerful random multiplexing mechanism for precoding, and adopts the linear-complexity orthogonal approximate message passing (OAMP) estimator for symbol detection, where the compatibility with the existing fifth generation (5G) architectures is fully preserved. We further provide the theoretical analysis based on the replica-symmetric (RS) formula. This analysis confirms the advantages of the proposed system with respect to the adopted compression ratios, where an interesting phase transition behavior is verified. Numerical results coincide with our analysis and demonstrate significant improvements in terms of achievable rates and bit error rate (BER) compared to conventional MIMO-OFDM counterpart, making the proposed scheme a promising solution to 6G and beyond wireless networks.

eess.SP↗
arXiv2026

Semantic Reconstruction and 3-D Detection via Learned Multi-Pair Fusion in RF Imaging

We consider a multistatic radio-frequency imaging problem with anisotropy, in which the reflection from a point depends on the positions of the transmit (Tx) and receive (Rx) arrays. The goal is to label the voxels of a field of view by a finite set of semantic classes and to group them into object instances. For the image formation of each Tx--Rx pair we apply a standard inverse-problem solver, and we feed the resulting per-pair reconstructions into a trained three-dimensional (3-D) U-Net that performs the fusion implicitly and the per-voxel classification explicitly. On a controlled, under-determined multistatic setup, we consider the following image formation methods: back-projection (BP) and the least absolute shrinkage and selection operator (LASSO) from a single deterministic snapshot, and incoherent BP and group-LASSO from multiple fading snapshots. For each imaging method we train a separate U-Net that fuses the six Tx--Rx pairs (its input channels) and assigns each voxel a probability vector over the classes. Taking the most probable class gives a labeled volume---the semantic reconstruction. Object instances and their oriented bounding boxes then follow by geometric post-processing (clustering and principal-component analysis). Across a wide range of signal-to-noise ratio, the semantic reconstruction (scored against ground truth by segmentation intersection-over-union) and the resulting 3-D detection degrade far more gracefully than the classical intensity reconstruction: the detection in particular stays reliable well into noise levels at which that reconstruction has dissolved. Because real scenes contain objects of classes the network was not trained on, we add an explicit unknown class trained by outlier exposure, which labels held-out novel objects as unknown instead of mislabeling them as a known class by reconstructed shape.

cs.CV↗
arXiv2026

Intelligent Wiretap Code Design: Exploiting Wireless Endogenous Security via Information Theory and Deep Learning Integration

Recent advancements in wireless endogenous security have explored leveraging the inherent randomness of wireless channels to enhance communication security, providing an effective alternative to traditional encryption methods. This paper proposes a wiretap coding scheme within the semantic communication framework, which leverages discrete semantic representations compatible with conventional digital modulation to jointly enhance communication security and reliability. We investigate two eavesdropping scenarios: (i) the eavesdropper employs a maximum a posteriori (MAP) decoder, and (ii) the eavesdropper has access to a decoder identical to that of the legitimate receiver. In the first scenario, we exploit mutual information as a metric to guide the design of an optimized coding strategy, minimizing information leakage while enhancing communication reliability. In the second scenario, considering the limitations of the eavesdropper's decoding capability, we employ generalized mutual information (GMI) to characterize recoverability under the prescribed decoding rule and guide reliability-aware code optimization.

cs.IT↗
arXiv2026

Near-Field Communications with Different Array Geometries: Rayleigh Distance, Channel Estimation, and Transmission Design

This work establishes a framework of near-field communication under different array geometries of extremely large-scale multiple-input multiple-output (XL-MIMO). We first formulate the near-field spatial non-stationary channel model which is characterized by the distance between the user and each antenna on uniform and modular curved arrays. By fixing the total number of antennas while varying the degree of curvature, we investigate a fair case where the horizontal arc length of the curved array is the same as the planar array. We explicitly unveil the non-trivial impact of array curvature on extending the near-field region for cell edges. Then, for arbitrary array geometries and arbitrary-field channels, we estimate the spatial-domain channel by tackling a compressed sensing problem with a learned regularizer. Without relying on specific codebooks, we propose a denoising autoencoder (AE)-aided approximated message passing (AMP) algorithm and provide the corresponding theoretical replica bound. Finally, based on the estimated channel, we propose an optimization algorithm to maximize the sum user rate for sub-connected XL-MIMO systems by jointly designing the array geometry and hybrid precoding in the downlink. Numerical results demonstrate that the proposed AE-AMP algorithm can effectively estimate the spatial non-stationary near-field channels with robustness and generalities compared to several conventional and deep-learning-based benchmarks. The improvement of data rate by using modular curved arrays with the estimated channel is also validated.

eess.SP↗
arXiv2026

A Superposition Signaling Scheme for Integrated Sensing and Communication

We investigate the capacity of an integrated sensing and communication system operating with orthogonal frequency division multiplexing, where the integrated sidelobe level of the transmit signal is adopted as an input cost. The problem is reduced to the capacity of a complex additive white Gaussian noise channel under power and kurtosis constraints. The sensing-optimal and communication-optimal operating points are characterized by circularly symmetric constant-modulus and complex Gaussian inputs, respectively, while the capacity-achieving input in between has discrete amplitude support with a concentric multi-ring geometry. We propose the superposition of the two extremal inputs as a simple and tunable signaling strategy, whose rate admits an exact expression via successive decoding. Numerical results show that the proposed strategy attains a worst-case gap of approximately 0.043 bits to the capacity.

cs.IT↗
arXiv2026

On the Gaussian-Quadratic Rate-Distortion Function for Vector Sources with Individual Distortion Constraints

This paper investigates the Gaussian-quadratic lossy compression with arbitrary source length under individual distortion constraints. The rate-distortion function (RDF) is lower-bounded by a Hadamard inequality-based rate, which is tight if and only if the semidefinite condition (SDC) holds. Otherwise, this bound becomes loose, and analytical results are lacking. Moreover, the fundamental quantitative relationship between source correlations and the RDF remains incomplete. In this paper, we provide new theoretical results under different source covariance matrices and distortion constraints. First, under arbitrary covariance and distortion constraints, we obtain the spectral properties of the optimal source reconstruction achieving the RDF, and a stronger scalar inequality version of the SDC. We propose a class of source covariance matrices based on hierarchical correlations and show that studying the two-type correlation (2-TC) model is sufficient to establish the analytical foundation for the broader class. Under this covariance, we obtain the RDF with source correlations explicitly incorporated when the SDC holds, and analyze the SDC from the perspectives of distortion constraints and source correlations. Next, under the 2-TC covariance and two-type distortion (2-TD) constraints, we establish the complete RDFs over seven regions on a distortion plane, with the optimal distortion (rate) allocations determined in each region. It is revealed that the essence of pursuing the complete RDF lies in thoroughly analyzing the correlations between the optimal distortions. Finally, under isotropic correlation and identical constraints, we provide the per-component compression rate and show that exploiting correlations can significantly reduce compression costs.

cs.IT↗
arXiv2026

Rethinking Fronthaul Topologies for Cell-Free 6G Networks

Due to significant progress in physical layer (PHY) technologies, future 6G networks are expected to feature much denser antenna deployments. Cell-free MIMO network designs are one of the most promising candidates to enable denser networks. While significant effort has been put into its PHY research, the challenges it brings to networking disciplines remain relatively unexplored. In this work, we propose various fronthaul network designs for cell-free MIMO. Our results show that while tree topologies may suffice for small-scale deployments, they become infeasible as the number of antennas increases. In contrast, with growing network size, the proposed Clos topology performs almost as well as the optimal topology.

cs.IT↗
arXiv2026

A Modular O-RAN Testbed Based on SRS Open Source O-CU/O-DU and Massive Beams Modular O-RU

In this paper, we present a modular open radio access network (O-RAN) consisting of the 5G Core, a central (O-CU) and distributed unit (O-DU) by Software Radio Systems (SRS) and an O-RAN radio unit (O-RU), MODRAD-SC, by Massive Beams (MB). OCUDU provides an open source 5G-compliant O-CU and O-DU solution developed by SRS, while MB's radio unit is a fully O-RAN compliant category A O-RU. According to O-RAN split 7.2a, OCUDU performs higher layer functions up to the high physical (PHY) layer, while the O-RU handles low PHY and RF functions. This results in an O-RAN-compliant 5G gNodeB. In an alternative configuration, OCUDU and MODRAD-SC operate in a software-defined radio fashion corresponding to split 8, facilitating non-real-time experiments among others. In both cases, the system provides full control over O-CU, O-DU, and O-RU. In addition, we will discuss the possibility to attach an analog beamformer to the O-RU, enabling hybrid digital-analog beamforming. The flexibility and modularity offered by OCUDU and MODRAD-SC enable the practical realization of a multitude of applications, ranging from 5G demonstrators to pre-6G experiments. The system addresses the requirements of academia and industry and is well-suited as an easy-to-use platform for experimental and practical deployments.

cs.NI↗
arXiv2026

Capacity-Region-Achieving Sparse Regression Codes for MIMO Multiple-Access Channels

This paper proposes a coding framework for capacity-region-achieving sparse regression (SR) codes over MIMO multiple-access channels (MIMO-MAC), where a single SR code is used for each user at the transmitter. With random semi-unitary dictionary matrices applied for encoding, multiple-access OAMP (MA-OAMP) enables reliable parallel interference cancellation (PIC) at the receiver. Theoretically, an optimal coding principle with the MA-OAMP receiver, which achieves the sum capacity and, in combination with time sharing, achieves the entire capacity region, is established as the guiding principle for designing capacity-region-achieving codes. Accordingly, a coding scheme for capacity-region-achieving SR codes is proposed via proper power allocation over the position-modulated signals.

cs.IT↗
arXiv2026

Rethinking Fractional Programming for Joint Uplink Scheduling and Power Control in Multicell Wireless Networks

This paper investigates the joint uplink scheduling and power control problem in a coordinated multicell wireless network, where at most one single-antenna user is allowed to access the single-antenna base station in each cell simultaneously. The resulting weighted sum-rate (WSR) maximization problem is a mixed discrete-continuous, nonconvex optimization problem that is notoriously difficult to solve directly. Classical fractional programming (FP) methods tackle this problem by leveraging the Lagrangian dual transform (LDT) followed by the quadratic transform (QT), yielding a tractable closed-form solution for scheduling and power control, with the LDT playing a crucial role in handling discrete variables. In this paper, we revisit the LDT from a minorization-maximization (MM) perspective and observe that its induced surrogate is somehow conservative due to the reciprocal-coordinate construction. Motivated by this observation, we propose a novel reciprocal-inversion transform (RIT) that constructs a tighter first-order Taylor expansion lower bound for the logarithmic rate function. The proposed RIT remains fully compatible with the QT, leading to a surrogate-enhanced FP (SEFP) algorithm for joint uplink scheduling and power control. The proposed SEFP algorithm retains the desirable per-cell separability of the classical FP framework and admits closed-form updates for the auxiliary variables, scheduling decisions, and transmit powers. Simulation results demonstrate that the SEFP algorithm consistently outperforms the classical FP method and other baselines for different network utilities.

eess.SP↗
arXiv2026

An Orthogonal Approximate Message Passing Framework for Multiuser Communications

We solve the open problem of constructing a Bayes-optimal iterative signal recovery algorithm for linear-Gaussian \emph{multiuser} communication systems with random precoding at the transmitters. Specifically, we consider the received signal model $\mathbf{y} = \sum_{u} \mathbf{H}_u \mathbfΞ_u \mathbf{s}_u + \mathbf{n}$, where $\mathbf{n}$ is white Gaussian noise, $\{\mathbf{H}_u \in \mathbb{C}^{L \times L}\}$ are discrete-time channel matrices -- modeling a wide class of generally time-varying and dispersive linear channels with possibly multiple antennas -- and the precoding matrices $\{\boldsymbolΞ_u \in \mathbb{C}^{L \times N_u}\}$ are drawn independently from a right-unitarily invariant random matrix ensemble. We consider generic \emph{non-separable} (coded) systems where the users' signals $\{\mathbf{s}_u\}$ follow general (non-factorizing) distributions. For this model, we introduce a novel orthogonal/vector approximate message passing (OAMP/VAMP)-type framework, including an algorithm and its high-dimensional (but finite-sample) analysis. From an algorithmic standpoint, the proposed method can be interpreted as an \emph{interpolation} between Minka's expectation propagation (EP)--a widely used method in machine learning--and OAMP. Our main theoretical contribution is the explicit finite-sample analysis of the proposed algorithm. Furthermore, we analyze the associated inference problem via a replica-symmetric (RS) ansatz by using a novel disorder-averaging technique. Both the (rigorous) high-dimensional analysis of the algorithm and the RS ansatz reveal the same decoupling principle, establishing that the proposed algorithm is asymptotically Bayes-optimal under the validity of the RS ansatz.

cs.IT↗
arXiv2026

On the Outage Probability of Multiuser Multiple Antenna Systems with Non-Orthogonal Multiple Access for Air-Ground Communications

This paper explores multiuser multiple antenna systems as a means to enhance the spectral efficiency of aeronautical communications systems. To this end, the outage regime for a multiuser multiple antenna system is studied within a realistic geometry-based stochastic air-ground (AG) channel model. In this application, users (aircraft) transmit air traffic management data to the ground station at a predefined target rate. Due to the nature of the AG propagation, we argue that the relevant performance metric in this context is the information outage probability. We consider the outage probability of individual aircraft under three decoding approaches. The first is based on successive interference cancellation (SIC). The second extends the first approach by considering joint group decoding. The third is a version of the second that limits the size of the jointly decoded user groups in order to lower the decoding complexity. The results show that joint group decoding, even in groups of only two, can significantly increase the spectral efficiency in the AG channel by allowing a large number of aircraft to transmit over a non-orthogonal channel with very low outage probabilities.

cs.IT↗
arXiv2026

Channel Estimation under Large Doppler Shifts and Channel Aging in NOMA-Based Air-Ground Communications

This paper investigates a multiple antenna system with non-orthogonal multiple access (NOMA) for the exchange of air traffic management data between commercial aircraft pilots and ground-based air traffic controllers. While NOMA techniques enhance spectral efficiency, their application to aircraft communications is challenged by the high speed of the aircraft (up to 214 m/s) and the long communication ranges (up to 250 km), resulting in significant Doppler shifts and low signal-to-noise ratios, respectively. To accurately assess these challenges, we employ a realistic geometry-based stochastic air-ground channel model, derived from dedicated flight measurement campaigns. In this paper, multiple aircraft simultaneously transmit data to the ground station. We focus on the channel estimation problem at the ground station under high carrier frequency offsets and the effects of channel aging due to channel's time-varying nature. For the channel estimation problem, we compare the Zadoff-Chu sequences with time-division approach under varying carrier frequency offset pre-compensation accuracies at the aircraft transmitter. For the channel aging problem and performance evaluation of channel estimators, we compute the outage probability for both the zero-forcing detector and the minimum mean squared error detector with successive interference cancellation. The results show that the favorable channel estimator-detector combinations differ between the takeoff & landing phase and the enroute cruise phase of the flight, due to the distinct channel propagation characteristics of each phase.

eess.SY↗
arXiv2026

Noncoherent ISAC over Block-Fading Channels: Asymptotic Performance Analysis

This paper investigates the fundamental limits and optimal signal distribution design for Integrated Sensing and Communication (ISAC) systems operating under strictly noncoherent conditions. Unlike conventional coherent frameworks that rely on perfect channel state information, we consider a block-fading MIMO channel where the channel realizations are unknown to both the transmitter and the receiver. We adopt a realization-wise perspective to characterize the noncoherent performance tradeoff across different signal-to-noise ratio (SNR) regimes. In the high-SNR regime, we derive a lower bound for the noncoherent mutual information and define a metric, termed sensing-induced rate loss, to quantify the communication penalty incurred by sensing-oriented beamforming. We then employ a projected gradient algorithm to optimize the spatial power allocation, balancing the conflict between the unitary space-time modulation-based structure for communication and the task-oriented spatial power allocation for sensing. Conversely, in the low-SNR regime, we perform a first-order asymptotic analysis of the ergodic minimum mean squared error (EMMSE). Our theoretical derivation reveals a fundamental synergy: the sensing-optimal strategy collapses to a rank-one transmission along the dominant eigenvector of the target response, which incurs no first-order communication loss in the low-SNR regime. This result demonstrates that the conflicting tradeoff observed at high SNR vanishes asymptotically at low SNR, enabling perfect alignment between sensing and communication objectives.

cs.IT↗
arXiv2026

Multi-view imaging in networked sensing systems: A covariance-based approach

This paper considers multi-view imaging in a sixth-generation (6G) integrated sensing and communication network, which consists of a transmit base-station (BS), multiple receive BSs connected to a central processing unit (CPU), and multiple extended targets. Our goal is to devise an effective multi-view imaging technique that can jointly leverage the targets' echo signals at all the receive BSs to precisely construct the image of these targets. To achieve this goal, we propose a two-phase approach. In Phase I, each receive BS recovers an individual image based on the sample covariance matrix of its received signals. Specifically, we propose a novel covariance-based imaging framework to jointly estimate effective scattering intensity and grid positions, which reduces the number of estimated parameters leveraging channel statistical properties and allows grid adjustment to conform to target geometry. In Phase II, the CPU fuses the individual images of all the receivers to construct a high-quality image of all the targets. Specifically, we design edge-preserving natural neighbor interpolation (EP-NNI) to map individual heterogeneous images onto common and finer grids, and then propose a joint optimization framework to estimate fused scattering intensity and BS fields of view. Extensive numerical results show that the proposed scheme significantly enhances imaging performance, facilitating high-quality environment reconstruction for future 6G networks.

eess.SP↗