arXiv subjects

Giuseppe Caire

Publications and source records attributed to Giuseppe Caire.

At least 109 records · Page 6Linked to original sources

arXiv2025

On Secrecy Capacity of Binary Beampointing Channels with Block Memory and Feedback

This paper investigates the secrecy capacity of the binary beampointing (BBP) channel with block memory and feedback, a simplified yet insightful model for millimeter-wave (mmWave) systems with beamformed transmissions and backscatter feedback. We consider a system where a legitimate receiver and a passive eavesdropper experience independent and uniformly distributed angular directions over transmission blocks, with the base station receiving noiseless, unit-delayed feedback from both, under the per-symbol input cost constraints. We establish a closed-form upper bound on the secrecy capacity, which is based on the main channel between the base station and the legitimate receiver. Moreover, we propose a joint communication and adaptive sensing (JCAS) scheme and derive its achievable secrecy rate. Simulation results show that the gap between the inner and outer bounds narrows as the number of block length increases. This reveals the efficiency of this JCAS scheme, which strategically leverages feedback to balance the demands of sensing the legitimate user and preventing information leakage to the eavesdropper.

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arXiv2025

On the Sensing Capacity of Gaussian "Beam-Pointing" Channels with Block Memory and Feedback

Driven by the demands of high-frequency wireless communications in 5G and 6G systems (e.g., mmWave, sub-THz), we explore a state-dependent {\em Gaussian beam-pointing} (GBP) channel. In this model, the channel state defines an unknown angle of departure (AoD), which remains constant within each coherence block of $Q$ time slots but changes independently across blocks. The transmitter receives strictly causal feedback which may originate from a radar detection system or explicit feedback from the receiver at the end of each slot and estimates the AoD at the end of each block. To enhance transmission efficiency, we propose a joint communication and sensing scheme. While the communication capacity of the GBP channel has been previously analyzed by the authors, this work focuses on sensing capacity, characterized by the mutual information between the channel state and the feedback conditioned on the transmitted signal. We derive an upper bound using dynamic programming and propose an achievable inner bound on the sensing capacity, both formulated as optimization problems. For the special case of $Q=1$, the proposed transmission scheme achieves the optimal sensing rate and highlights the inherent trade-off between sensing and communication performance.

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arXiv2025

Near-Field Integrated Imaging and Communication in Distributed MIMO Networks

In this work, we propose a general framework for wireless imaging in distributed MIMO wideband communication systems, considering multi-view non-isotropic targets and near-field propagation effects. For indoor scenarios where the objective is to image small-scale objects with high resolution, we propose a range migration algorithm (RMA)-based scheme using three kinds of array architectures: the full array, boundary array, and distributed boundary array. With non-isotropic near-field channels, we establish the Fourier transformation (FT)-based relationship between the imaging reflectivity and the distributed spatial-domain signals and discuss the corresponding theoretical properties. Next, for outdoor scenarios where the objective is to reconstruct the large-scale three-dimensional (3D) environment with coarse resolution, we propose a sparse Bayesian learning (SBL)-based algorithm to solve the multiple measurement vector (MMV) problem, which further addresses the non-isotropic reflectivity across different subcarriers. Numerical results demonstrate the effectiveness of the proposed algorithms in acquiring high-resolution small objects and accurately reconstructing large-scale environments.

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arXiv2025

Fundamental Limits of Distributed Linearly Separable Computation under Cyclic Assignment

This paper studies the master-worker distributed linearly separable computation problem, where the considered computation task, referred to as linearly separable function, is a typical linear transform model widely used in cooperative distributed gradient coding, real-time rendering, linear transformers, etc. %A master asks $\Nsf$ distributed workers to compute a linearly separable function from $\Ksf$ datasets. The computation task on $\Ksf$ datasets can be expressed as $\Ksf_{\rm c}$ linear combinations of $\Ksf$ messages, where each message is the output of an individual function on one dataset. Straggler effect is also considered, such that from the answers of any $\Nsf_{\rm r}$ of the $\Nsf$ distributed workers, the master should accomplish the task. The computation cost is defined as the number of datasets assigned to each worker, while the communication cost is defined as the number of (coded) messages that should be received. The objective is to characterize the optimal tradeoff between the computation and communication costs. The problem has remained so far open, even under the cyclic data assignment.Since in fact various distributed computing schemes were proposed in the literature under the cyclic data assignment, with this paper we close the problem for the cyclic assignment. This paper proposes a new computing scheme with the cyclic assignment based on the concept of interference alignment, by treating each message which cannot be computed by a worker as an interference from this worker. Under the cyclic assignment, the proposed computing scheme is then proved to be optimal when $\Nsf=\Ksf$ and be order optimal within a factor of $2$ otherwise.

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arXiv2025

Frequency-Space Channel Estimation and Spatial Equalization in Wideband Fluid Antenna System

The Fluid Antenna System (FAS) overcomes the spatial degree-of-freedom limitations of conventional static antenna arrays in wireless communications.This capability critically depends on acquiring full Channel State Information across all accessible ports. Existing studies focus exclusively on narrowband FAS, performing channel estimation solely in the spatial domain. This work proposes a channel estimation and spatial equalization framework for wideband FAS, revealing for the first time an inherent group-sparse structure in aperture-limited FAS channels. First, we establish a group-sparse recovery framework for space-frequency characteristics in FAS, formally characterizing leakage-induced sparsity degradation from limited aperture and bandwidth as a structured group-sparsity problem. By deriving dictionary-adapted group restricted isometry property, we prove tight recovery bounds for a convex $\ell_1/\ell_2$-mixed norm optimization formulation that preserves leakage-aware sparsity patterns. Second, we develop a descending correlation group orthogonal matching pursuit algorithm that systematically relaxes leakage constraints to reduce subcoherence. This approach enables FSC recovery with accelerated convergence and superior performance compared to conventional compressive sensing methods like OMP or GOMP. Third, we formulate spatial equalization as a mixed-integer linear programming problem, complement this with a greedy algorithm maintaining near-optimal performance. Simulation results demonstrate the proposed channel estimation algorithm effectively resolves energy misallocation and enables recovery of weak details, achieving superior recovery accuracy and convergence rate. The SE framework suppresses deep fading phenomena and largely reduces time consumption overhead while maintaining equivalent link reliability.

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arXiv2025

Achievable Rates for a Distributed Antenna System with No Channel State Information at the Central Processor

A recent trend in wireless communications considers the migration of traditional monolithic base stations to the so-called disaggregated architecture, where radio units (RUs) implement only the low-level physical layer functionalities such as demodulation, and A/D conversion, while the high-level physical layer, such as channel decoding, is implemented as software-defined functions running on general-purpose hardware in some remote central processing unit (CP). The corresponding information theoretic model for the uplink (from the wireless users to the CP) is a multiaccess-relay channel with primitive oblivious relays. The relays (RUs) are oblivious, as they are agnostic of the users codebooks, and primitive, since the fronthaul links (from RUs to CP) are error-free with limited capacity. This class of networks has been intensely studied in the information theoretic literature, where several approximated or exact (under certain conditions) capacity results have been derived. In particular, in the Gaussian case, the model has been analyzed for fixed and known channel state. This paper is motivated by the fact that, in practice, the channel state is a random process, and it is estimated at the base station side through uplink pilot symbols sent by the users. The pilot dimension may take up a large portion of the channel coherence block, i.e., the number of symbols over which the channel state remains approximately constant. Hence, sending both pilot and data symbols from the relays to the CP may require a significant overhead, especially when the fronthaul capacity is small. As a prototypical problem, we consider the ergodic achievable rate for a diamond network formed by a single user and two relays where the channel state is known at the relays, but not known at the CP.

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arXiv2025

CP-OFDM Achieves the Lowest Average Ranging Sidelobe Under QAM/PSK Constellations

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC): What is the optimal communication-centric ISAC waveform for ranging? Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

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arXiv2025

Blind and Topological Interference Managements for Bistatic Integrated Sensing and Communication

Integrated sensing and communication (ISAC) systems provide significant enhancements in performance and resource efficiency compared to individual sensing and communication systems, primarily attributed to the collaborative use of wireless resources, radio waveforms, and hardware platforms. This paper focuses on the bistatic ISAC systems with dispersed multi-receiver and one sensor. Compared to a monostatic ISAC system, the main challenge in the bistatic setting is that the information messages are unknown to the sensor and therefore they are seen as interference, while the channel between the transmitters (TX) and the sensor is unknown to the transmitters. In order to mitigate the interference at the sensor while maximizing the communication degree of freedom, we introduce two strategies, namely, blind interference alignment and topological interference management. Although well-known in the context of Gaussian interference channels, these strategies are novel in the context of bistatic ISAC. For the bistatic ISAC models with heterogeneous coherence times or with heterogeneous connectivity, the achieved ISAC tradeoff points in terms of communication and sensing degrees of freedom are characterized. In particular, we show that the new tradeoff outperforms the time-sharing between the sensing-only and the communication-only schemes. Simulation results demonstrate that the proposed schemes significantly improve the channel estimation error for the sensing task, compared to treating interference as noise at the sensor and successive interference cancellation.

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arXiv2025

A joint channel estimation and beamforming separation principle for massive MIMO systems

We demonstrate that separating beamforming (i.e., downlink precoding and uplink combining) and channel estimation in multi-user MIMO wireless systems incurs no loss of optimality under general conditions that apply to a wide variety of models in the literature, including canonical reciprocity-based cellular and cell-free massive MIMO system models. Specifically, we provide conditions under which optimal processing in terms of ergodic achievable rates can be decomposed into minimum mean-square error (MMSE) channel estimation followed by MMSE beamforming, for both centralized and distributed architectures. Applications of our results are illustrated in terms of concrete examples and numerical simulations.

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arXiv2025

Radio Map Prediction from Aerial Images and Application to Coverage Optimization

Several studies have explored deep learning algorithms to predict large-scale signal fading, or path loss, in urban communication networks. The goal is to replace costly measurement campaigns, inaccurate statistical models, or computationally expensive ray-tracing simulations with machine learning models that deliver quick and accurate predictions. We focus on predicting path loss radio maps using convolutional neural networks, leveraging aerial images alone or in combination with supplementary height information. Notably, our approach does not rely on explicit classification of environmental objects, which is often unavailable for most locations worldwide. While the prediction of radio maps using complete 3D environmental data is well-studied, the use of only aerial images remains under-explored. We address this gap by showing that state-of-the-art models developed for existing radio map datasets can be effectively adapted to this task. Additionally, we introduce a new model dubbed UNetDCN that achieves on par or better performance compared to the state-of-the-art with reduced complexity. The trained models are differentiable, and therefore they can be incorporated in various network optimization algorithms. While an extensive discussion is beyond this paper's scope, we demonstrate this through an example optimizing the directivity of base stations in cellular networks via backpropagation to enhance coverage.

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arXiv2025

Downlink CSIT under Compressed Feedback: Joint vs. Separate Source-Channel Coding

The acquisition of Downlink (DL) channel state information at the transmitter (CSIT) is known to be a challenging task in multiuser massive MIMO systems when uplink/downlink channel reciprocity does not hold (e.g., in frequency division duplexing systems). From a coding viewpoint, the DL channel state acquired at the users via DL training can be seen as an information source that must be conveyed to the base station via the UL communication channels. The transmission of a source through a channel can be accomplished either by separate or joint source-channel coding (SSCC or JSCC). In this work, using classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation mean-square-error (MSE) of both JSCC and SSCC-based feedback schemes, which however requires encoding of large blocks of successive channel states and thus cannot be used in practicesince it would incur in an extremely large feedback delay. We then focus on the relevant case of minimal (one slot) feedback delay and propose a practical JSCC-based feedback scheme that fully exploits the channel second-order statistics to optimize the dimension projection in the eigenspace. We analyze the large SNR behavior of the proposed JSCC-based scheme in terms of the quality scaling exponent (QSE). Given the second-order statistics of channel estimation of any feedback scheme, we further derive the closed-form of the lower bound to the ergodic sum-rate for DL data transmission under maximum ratio transmission and zero-forcing precoding. Via extensive numerical results, we show that our proposed JSCC-based scheme outperforms known JSCC, SSCC baseline and deep learning-based schemes and is able to approach the performance of the optimal DR scheme in the range of practical SNR.

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arXiv2025

Faster-than-Nyquist Signaling is Good for Single-Carrier ISAC: An Analytical Study

In this paper, we provide an analytical study of single-carrier faster-than-Nyquist (FTN) signaling for integrated sensing and communications (ISAC). Our derivations show that FTN is advantageous for ISAC, and reveal new insights that these advantages come from the fact that FTN signaling can effectively avoid the spectral aliasing due to the mismatch between the symbol rate and the bandwidth of the shaping pulse. Specifically, the communication spectral efficiency advantages of FTN signaling over time-invariant multipath channels are analytically shown, where both upper- and lower-bounds on the spectral efficiency are derived. We show that the gap between these two bounds corresponds to the potential signal-to-noise ratio (SNR) variation due to the presence of multipath delay and spectral aliasing, which diminishes as the symbol rate grows higher. Particularly, in the limiting case, this SNR variation disappears while the degree of freedom (DoF) of the system attain the maximum. Furthermore, the sensing advantages for FTN signals are verified in terms of the expected normalized squared ambiguity function. We show that FTN signals generally enjoy a more robust ranging performance. More importantly, we prove that FTN signaling can effectively avoid the undesired peaks in the considered ambiguity function along the Doppler dimension, thereby reducing the ambiguities in velocity estimation. All these conclusions are explicitly verified by numerical results.

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arXiv2025

ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment

In this paper, we propose ByzSecAgg, an efficient secure aggregation scheme for federated learning that is resistant to Byzantine attacks and privacy leakages. Processing individual updates to manage adversarial behavior, while preserving the privacy of the data against colluding nodes, requires some sort of secure secret sharing. However, the communication load for secret sharing of long vectors of updates can be very high. In federated settings, where users are often edge devices with potential bandwidth constraints, excessive communication overhead is undesirable. ByzSecAgg solves this problem by partitioning local updates into smaller sub-vectors and sharing them using ramp secret sharing. However, this sharing method does not admit bilinear computations, such as pairwise distances calculations, which are needed for distance-based outlier-detection algorithms, and effective methods for mitigating Byzantine attacks. To overcome this issue, each user runs another round of ramp sharing, with a different embedding of the data in the sharing polynomial. This technique, motivated by ideas from coded computing, enables secure computation of pairwise distance. In addition, to maintain the integrity and privacy of the local update, ByzSecAgg also uses a vector commitment method, in which the commitment size remains constant (i.e., does not increase with the length of the local update), while simultaneously allowing verification of the secret sharing process. In terms of communication load, ByzSecAgg significantly outperforms the related baseline scheme, known as BREA.

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arXiv2025

PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning

Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data while preserving user privacy. However, the typical paradigm of FL faces challenges of both privacy and robustness: the transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates. Current solutions attempting to address both problems under the one-server FL setting fall short in the following aspects: 1) designed for simple validity checks that are insufficient against advanced attacks (e.g., checking norm of individual update); and 2) partial privacy leakage for more complicated robust aggregation algorithms (e.g., distances between model updates are leaked for multi-Krum). In this work, we formalize a novel security notion of aggregated privacy that characterizes the minimum amount of user information, in the form of some aggregated statistics of users' updates, that is necessary to be revealed to accomplish more advanced robust aggregation. We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy. As concrete instantiations of PriRoAgg, we construct two secure and robust protocols based on state-of-the-art robust algorithms, for which we provide full theoretical analyses on security and complexity. Extensive experiments are conducted for these protocols, demonstrating their robustness against various model integrity attacks, and their efficiency advantages over baselines.

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arXiv2025

Collaborative Coded Caching for Partially Connected Networks

Coded caching leverages the differences in user cache memories to achieve gains that scale with the total cache size, alleviating network congestion due to high-quality content requests. Additionally, distributing transmitters over a wide area can mitigate the adverse effects of path loss. In this work, we consider a partially connected network where the channel between distributed transmitters (helpers) and users is modeled as a distributed multiple-input-multiple-output (MIMO) Gaussian broadcast channel. We propose a novel delivery scheme consisting of two phases: partitioning and transmission. In the partitioning phase, users with identical cache profiles are partitioned into the minimum number of sets, such that users within each set can successfully decode their desired message from a joint transmission enabled by MIMO precoding. To optimally partition the users, we employ the branch and bound method. In the transmission phase, each partition is treated as a single entity, and codewords are multicast to partitions with distinct cache profiles. The proposed delivery scheme is applicable to any partially connected network, and while the partitioning is optimal, the overall delivery scheme, including transmission, is heuristic. Interestingly, simulation results show that its performance closely approximates that of the fully connected optimal solution.

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arXiv2025

Joint Message Detection and Channel Estimation for Unsourced Random Access in Cell-Free User-Centric Wireless Networks

We consider unsourced random access (uRA) in a cell-free (CF) user-centric wireless network, where a large number of potential users compete for a random access slot, while only a finite subset is active. The random access users transmit codewords of length $L$ symbols from a shared codebook, which are received by $B$ geographically distributed radio units (RUs) equipped with $M$ antennas each. Our goal is to devise and analyze a \emph{centralized} decoder to detect the transmitted messages (without prior knowledge of the active users) and estimate the corresponding channel state information. A specific challenge lies in the fact that, due to the geographically distributed nature of the CF network, there is no fixed correspondence between codewords and large-scale fading coefficients (LSFCs). This makes current activity detection approaches which make use of this fixed LSFC-codeword association not directly applicable. To overcome this problem, we propose a scheme where the access codebook is partitioned in location-based subcodes, such that users in a particular location make use of the corresponding subcode. The joint message detection and channel estimation is obtained via a novel {\em Approximated Message Passing} (AMP) algorithm for a linear superposition of matrix-valued sources corrupted by noise. The statistical asymmetry in the fading profile and message activity leads to \emph{different statistics} for the matrix sources, which distinguishes the AMP formulation from previous cases. In the regime where the codebook size scales linearly with $L$, while $B$ and $M$ are fixed, we present a rigorous high-dimensional (but finite-sample) analysis of the proposed AMP algorithm. Exploiting this, we then present a precise (and rigorous) large-system analysis of the message missed-detection and false-alarm rates, as well as the channel estimation mean-square error.

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arXiv2025

Flat-Top Beamforming with Efficient Array-Fed RIS

Flat-top beam designs are essential for uniform power distribution over a wide angular sector for applications such as 5G/6G networks, beaconing, satellite communications, radar systems, etc. Low sidelobe levels with steep transitions allow negligible cross sector illumination. Active array designs requiring amplitude taper suffer from poor power amplifier utilization. Phase only designs, e.g., Zadoff-Chu or generalized step chirp polyphase sequence methods, often require large active antenna arrays which in turns increases the hardware complexity and reduces the energy efficiency. In our recently proposed novel array-fed reflective intelligent surface (RIS) architecture, the small ($2 \times 2$) active array has uniform (principal eigenmode) amplitude weighting. We now present a pragmatic flat-top pattern design method for practical array (RIS) sizes, which outperforms current state-of-the-art in terms of design superiority, energy efficiency, and deployment feasibility. This novel design holds promise for advancing sustainable wireless technologies in next-generation communication systems, including applications such as beaconing, broadcast signaling, and hierarchical beamforming, while mitigating the environmental impact of high-energy antenna arrays.

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arXiv2025

Complexity-Scalable Near-Optimal Transceiver Design for Massive MIMO-BICM Systems

Future wireless networks are envisioned to employ multiple-input multiple-output (MIMO) transmissions with large array sizes, and therefore, the adoption of complexity-scalable transceiver becomes important. In this paper, we propose a novel complexity-scalable transceiver design for MIMO systems exploiting bit-interleaved coded modulation (termed MIMO-BICM systems). The proposed scheme leverages the channel bidiagonalization decomposition (CBD), based on which an optimization framework for the precoder and post-processor is developed for maximizing the mutual information (MI) with finite-alphabet inputs. Particularly, we unveil that the desired precoder and post-processor behave distinctively with respect to the operating signal-to-noise ratio (SNR), where the equivalent channel condition number (ECCN) serves as an effective indicator for the overall achievable rate performance. Specifically, at low SNRs, diagonal transmission with a large ECCN is advantageous, while at high SNRs, uniform subchannel gains with a small ECCN are preferred. This allows us to further propose a low-complexity generalized parallel CBD design (GP-CBD) based on Givens rotation according to a well-approximated closed-form performance metric on the achievable rates that takes into account the insights from the ECCN. Numerical results validate the superior performance of the proposed scheme in terms of achievable rate and bit error rate (BER), compared to state-of-the-art designs across various modulation and coding schemes (MCSs).

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