arXiv subjects

Giuseppe Caire

Publications and source records attributed to Giuseppe Caire.

At least 91 records · Page 5Linked to original sources

arXiv2026

Faster-than-Nyquist Signaling for Next-Generation Wireless: Principles, Applications, and Challenges

Future wireless networks are expected to deliver ultra-high throughput for supporting emerging applications. In such scenarios, conventional Nyquist signaling may falter. As a remedy, faster-than-Nyquist (FTN) signaling facilitates the transmission of more symbols than Nyquist signaling without expanding the time-frequency resources. We provide an accessible and structured introduction to FTN signaling, covering its core principles, theoretical foundations, unique advantages, open facets, and its road map. Specifically, we present promising coded FTN results and highlight its compelling advantages in integrated sensing and communications (ISAC), an increasingly critical function in future networks. We conclude with a discussion of open research challenges and promising directions.

cs.IT↗

arXiv2025

Finite-Blocklength Information Theory

Traditional asymptotic information-theoretic studies of the fundamental limits of wireless communication systems primarily rely on some ideal assumptions, such as infinite blocklength and vanishing error probability. While these assumptions enable tractable mathematical characterizations, they fail to capture the stringent requirements of some emerging next-generation wireless applications, such as ultra-reliable low latency communication and ultra-massive machine type communication, in which it is required to support a much wider range of features including short-packet communication, extremely low latency, and/or low energy consumption. To better support such applications, it is important to consider finite-blocklength information theory. In this paper, we present a comprehensive review of the advances in this field, followed by a discussion on the open questions. Specifically, we commence with the fundamental limits of source coding in the non-asymptotic regime, with a particular focus on lossless and lossy compression in point-to-point~(P2P) and multiterminal cases. Next, we discuss the fundamental limits of channel coding in P2P channels, multiple access channels, and emerging massive access channels. We further introduce recent advances in joint source and channel coding, highlighting its considerable performance advantage over separate source and channel coding in the non-asymptotic regime. In each part, we review various non-asymptotic achievability bounds, converse bounds, and approximations, as well as key ideas behind them, which are essential for providing engineering insights into the design of future wireless communication systems.

cs.IT↗

arXiv2025

Graph-Theoretic Characterization of Noise Capacity of Conditional Disclosure of Secrets

In the Conditional Disclosure of Secrets (CDS) problem, Alice and Bob hold inputs $x\in \mathcal{X}$ and $y\in \mathcal{Y}$ and share a secret. Let $f:\mathcal{X}\times\mathcal{Y}\to\{0,1\}$ be a function such that the secret is revealed to a third party, Carol, if and only if $f(x,y)=1$. To protect the secret when $f(x,y)=0$, Alice and Bob share a common noise variable unknown to Carol. We study the \emph{noise capacity} of CDS, defined as the maximum number of secret bits that can be securely revealed per noise bit. We first derive necessary and sufficient conditions on $f$, represented by a CDS graph, for the extremal case where the noise capacity equals $1$. We then develop converse bounds on the noise rate for all linear schemes: $\frac{(ρ-1)(d-1)}{ρd-1}$ if $ρ$ is finite, and $\frac{d-1}{d}$ if $ρ$ is infinite, where $ρ$ is the covering parameter of the CDS graph and $d$ is the number of unqualified edges in an unqualified path. Under maximal communication efficiency (message size equals secret size), we refine these bounds by analyzing qualified components and their connections. Achievability is shown for CDS instances with cyclic qualified edges and a single unqualified path. This graph-theoretic framework links noise efficiency limits to the unqualified path distance and covering parameter, providing a systematic method to analyze CDS under arbitrary graph topologies.

cs.IT↗

arXiv2025

Optimal Key Rates for Decentralized Secure Aggregation with Arbitrary Collusion and Heterogeneous Security Constraints

Decentralized secure aggregation (DSA) considers a fully-connected network of $K$ users, where each pair of users can communicate bidirectionally over an error-free channel. Each user holds a private input, and the goal is for each user to compute the sum of all inputs without revealing any additional information, even in the presence of collusion among up to $T$ users. Traditional DSA typically requires large key sizes to protect all information except for the input sum and the information of colluding users. To mitigate the source keys overhead, we study decentralized secure aggregation with arbitrary collusion and heterogeneous security constraints. In this setting, the inputs of a predefined collection of user subsets, called the \emph{security set} $\bm{\mathcal{S}}$, must be protected from another predefined collection, the \emph{collusion set} $\bm{\mathcal{T}}$. For an arbitrary security set $\mathcal{S}\in \bm{\mathcal{S}}$ and an arbitrary collusion set $\mathcal{T}\in \bm{\mathcal{T}}$, we characterize the optimal communication and source key rates. A key contribution of this work is the characterization of the optimal source key rate, i.e., the minimum number of key bits per input bit that must be shared among users for decentralized secure aggregation with arbitrary collusion and heterogeneous security constraints to be feasible. In general, this characterization reduces to solving a linear program.

cs.IT↗

arXiv2025

Hybrid Iterative Detection for OTFS: Interplay between Local L-MMSE and Global Message Passing

Orthogonal time frequency space (OTFS) modulation has emerged as a robust solution for high-mobility wireless communications. However, conventional detection algorithms, such as linear equalizers and message passing (MP) methods, either suffer from noise enhancement or fail under complex doubly-selective channels, especially in the presence of fractional delay and Doppler shifts. In this paper, we propose a hybrid low-complexity iterative detection framework that combines linear minimum mean square error (L-MMSE) estimation with MP-based probabilistic inference. The key idea is to apply a new delay-Doppler (DD) commutation precoder (DDCP) to the DD domain signal vector, such that the resulting effective channel matrix exhibits a structured form with several locally dense blocks that are sparsely inter-connected. This precoding structure enables a hybrid iterative detection strategy, where a low-dimensional L-MMSE estimation is applied to the dense blocks, while MP is utilized to exploit the sparse inter-block connections. Furthermore, we provide a detailed complexity analysis, which shows that the proposed scheme incurs lower computational cost compared to the full-size L-MMSE detection. The simulation results of convergence performance confirm that the proposed hybrid MP detection achieves fast and reliable convergence with controlled complexity. In terms of error performance, simulation results demonstrate that our scheme achieves significantly better bit error rate (BER) under various channel conditions. Particularly in multipath scenarios, the BER performance of the proposed method closely approaches the matched filter bound (MFB), indicating its near-optimal error performance.

eess.SP↗

arXiv2025

Hierarchical Gradient Coding: From Optimal Design to Privacy at Intermediate Nodes

Gradient coding is a distributed computing technique for computing gradient vectors over large datasets by outsourcing partial computations to multiple workers, typically connected directly to the server. In this work, we investigate gradient coding in a hierarchical setting, where intermediate nodes sit between the server and workers. This structure reduces the communication load received at the server, which is a bottleneck in conventional gradient coding systems. In this paper, the intermediate nodes, referred to as \textit{relays}, process the data received from workers and send the results to the server for the final gradient computation. Our main contribution is deriving the optimal communication-computation trade-off by designing a linear coding scheme, also considering straggling and adversarial nodes among both relays and workers. We propose a coding scheme which achieves both the optimal relay-to-server communication load and the optimal worker-to-relay communication load. We further extend our setting to incorporate privacy by requiring that relays learn no information about the computed partial gradients from the messages they receive. This is achieved by introducing shared randomness among workers, allowing each worker to encode its partial gradients such that the randomness cannot be canceled out at the relay. Meanwhile, the server can successfully decode the global gradient by eliminating this randomness after receiving the computations of the non-straggling relays. Importantly, this privacy guarantee is achieved without increasing the overall communication load.

cs.IT↗

arXiv2025

The Capacity of Collusion-Resilient Decentralized Secure Aggregation with Groupwise Keys

This paper investigates the information-theoretic decentralized secure aggregation (DSA) problem under practical groupwise secret keys and collusion resilience. In DSA, $K$ users are interconnected through error-free broadcast channels. Each user holds a private input and aims to compute the sum of all other users' inputs, while satisfying the security constraint that no user, even when colluding with up to $T$ other users, can infer any information about the inputs beyond the recovered sum. To ensure security, users are equipped with secret keys to mask their inputs. Motivated by recent advances in efficient group-based key generation protocols, we consider the symmetric groupwise key setting, where every subset of $G$ users shares a group key that is independent of all other group keys. The problem is challenging because the recovery and security constraints must hold simultaneously for all users, and the structural constraints on the secret keys limit the flexibility of key correlations. We characterize the optimal rate region consisting of all achievable pairs of per-user broadcast communication rate and groupwise key rate. In particular, we show that DSA with groupwise keys is infeasible when $G=1$ or $G\ge K-T$. Otherwise, when $2\le G<K-T$, to securely compute one symbol of the desired sum, each user must broadcast at least one symbol, and each group key must contain at least $(K-T-2)/\binom{K-T-1}{G}$ independent symbols. Our results establish the fundamental limits of DSA with groupwise keys and provide design insights for communication- and key-efficient secure aggregation in decentralized learning systems.

cs.IT↗

arXiv2025

Reconfigurable Intelligent Surface-Assisted Multiple-Antenna Coded Caching

Reconfigurable Intelligent Surface (RIS) has emerged as a promising technology to enhance the wireless propagation environment for next-generation wireless communication systems. This paper introduces a new RIS-assisted multiple-antenna coded caching problem. Unlike the existing multi-antenna coded caching models, our considered model incorporates a passive RIS with a limited number of elements aimed at enhancing the multicast gain (i.e., Degrees of Freedom (DoF)). The system consists of a server equipped with multiple antennas and several single-antenna users. The RIS, which functions as a passive and configurable relay, improves communication by selectively erasing certain transmission paths between transmit and receive antennas, thereby reducing interference. We first propose a new RIS-assisted interference nulling algorithm to determine the phase-shift coefficients of the RIS. This algorithm achieves faster convergence compared to the existing approach. By strategically nulling certain interference paths in each time slot, the transmission process is divided into multiple interference-free groups. Each group consists of a set of transmit antennas that serve a corresponding set of users without any interference from other groups. The optimal grouping strategy to maximize the DoF is formulated as a combinatorial optimization problem. To efficiently solve this, we design a low-complexity algorithm that identifies the optimal solution and develops a corresponding coded caching scheme to achieve the maximum DoF. Building on the optimal grouping strategy, we introduce a new framework, referred to as RIS-assisted Multiple-Antenna Placement Delivery Array (RMAPDA), to construct the cache placement and delivery phases. Then we propose a general RMAPDA design to achieve the maximum DoF under the optimal grouping strategy.

cs.IT↗

arXiv2025

Near-Field 3D Localization and MIMO Channel Estimation with Sub-Connected Planar Arrays

This paper investigates the design of channel estimation and 3D localization algorithms in a challenging scenario, where a sub-connected planar extremely large-scale multiple-input multiple-output (XL-MIMO) communicates with multi-antenna users. In the near field, the uplink MIMO channel is of full column rank and therefore can not be estimated effectively by applying existing codebooks that are designed for the far-field case or for the near-field case but limited to single antenna users. To solve this problem, we propose a three-stage algorithm aided by orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL). Specifically, we firstly partition the XL-MIMO into subarrays and use OMP to solve the compressed sensing (CS) problem about subarray channel estimation with the Discrete Fourier Transform (DFT)-based dictionary matrix. Secondly, exploiting the estimated subarray channels and employing one-dimensional multiple signal classification (MUSIC), we estimate the central location of the user array under the Least Squares (LS) criterion. Finally, we utilize the estimated central location to construct a refined location-aided dictionary matrix and obtain the MIMO channel estimation using SBL. Results exhibit the significant superiority of the proposed algorithm compared with several benchmarks, in terms of both the pilot overhead and estimation accuracy.

eess.SP↗

arXiv2025

Beam Index Map Prediction in Unseen Environments from Geospatial Data

In 5G, beam training consists of the efficient association of users to beams for a given beamforming codebook used at the base station and the given propagation environment in the cell. We propose a convolutional neural network approach that leverages the position of the base station and geospatial data to predict beam distributions for all user locations simultaneously. Our method generalizes to unseen environments without site-specific training or specialized sensors. The results show that it significantly reduces the number of candidate beams considered, thereby improving the efficiency of beam training.

eess.SP↗

arXiv2025

ProxySelect: Frequency Selectivity-Aware Scheduling for Joint OFDMA and MU-MIMO in 802.11ax WiFi

IEEE 802.11ax introduces orthogonal frequency division multiple access (OFDMA) to WiFi to support concurrent transmissions to a larger number of users. As bandwidth continues to grow, WiFi channels exhibit increased frequency selectivity, which poses new challenges for MU-MIMO user selection: the optimal user set varies across frequency and is interleaved over subbands (called resource units, or RUs). This frequency selectivity, coupled with the complex subband allocation pattern, renders conventional narrowband user selection algorithms inefficient for 802.11ax. In this paper, we propose \emph{ProxySelect}, a scalable and frequency selectivity-aware user scheduling algorithm for joint OFDMA and MU-MIMO usage in 802.11ax under zero-forcing beamforming (ZFBF). The scheduling task is formulated as an integer linear program (ILP) with binary variables indicating user (group)-RU associations, and linear constraints ensuring standard compatibility. To reduce complexity, we introduce a novel proxy rate--a function of individual channel strengths and their correlations--that approximates the ZFBF rate without requiring cubic-complexity matrix inversion. Additionally, we develop a sampling-based candidate group generation scheme that selects up to $T$ near-orthogonal user groups for each RU, thereby bounding the ILP size and ensuring scalability. Simulations using realistic ray-tracing-based channel models show that ProxySelect achieves near-optimal rate performance with significantly lower complexity.

cs.IT↗

arXiv2025

Near-Field Imaging by Exploiting Frequency Correlation in Wireless Communication Networks

In this work, we address the near-field imaging under a wideband wireless communication network by exploiting both the near-field channel of a uniform linear array (ULA) and the image correlation in the frequency domain. We first formulate the image recovery as a special multiple measurement vector (MMV) compressed sensing (CS) problem, where at various frequencies the sensing matrices can be different, and the image coefficients are correlated. To solve such an MMV problem with various sensing matrices and correlated coefficients, we propose a sparse Bayesian learning (SBL)-based solution to simultaneously estimate all image coefficients and their correlation on multiple frequencies. Moreover, to enhance estimation performance, we design two illumination patterns following two different criteria. From the CS perspective, the first design minimizes the total coherence of the sensing matrix to increase the mutual orthogonality of the basis vectors. Alternatively, to improve SNR, the second design maximizes the illumination power of the imaging area. Numerical results demonstrate the effectiveness of the proposed SBL-based method and the superiority of the illumination designs.

cs.IT↗

arXiv2025

Integrated Massive Communication and Target Localization in 6G Cell-Free Networks

This paper presents an initial investigation into the combination of integrated sensing and communication (ISAC) and massive communication, both of which are largely regarded as key scenarios in sixth-generation (6G) wireless networks. Specifically, we consider a cell-free network comprising a large number of users, multiple targets, and distributed base stations (BSs). In each time slot, a random subset of users becomes active, transmitting pilot signals that can be scattered by the targets before reaching the BSs. Unlike conventional massive random access schemes, where the primary objectives are device activity detection and channel estimation, our framework also enables target localization by leveraging the multipath propagation effects introduced by the targets. However, due to the intricate dependency between user channels and target locations, characterizing the posterior distribution required for minimum mean-square error (MMSE) estimation presents significant computational challenges. To handle this problem, we propose a hybrid message passing-based framework that incorporates multiple approximations to mitigate computational complexity. Numerical results demonstrate that the proposed approach achieves high-accuracy device activity detection, channel estimation, and target localization simultaneously, validating the feasibility of embedding localization functionality into massive communication systems for future 6G networks.

eess.SP↗

arXiv2025

Multi-Message Secure Aggregation with Demand Privacy

This paper considers a multi-message secure aggregation with privacy problem, in which a server aims to compute $\sf K_c\geq 1$ linear combinations of local inputs from $\sf K$ distributed users. The problem addresses two tasks: (1) security, ensuring that the server can only obtain the desired linear combinations without any else information about the users' inputs, and (2) privacy, preventing users from learning about the server's computation task. In addition, the effect of user dropouts is considered, where at most $\sf{K-U}$ users can drop out and the identity of these users cannot be predicted in advance. We propose two schemes for $\sf K_c$ is equal to (1) and $\sf 2\leq K_c\leq U-1$, respectively. For $\sf K_c$ is equal to (1), we introduce multiplicative encryption of the server's demand using a random variable, where users share coded keys offline and transmit masked models in the first round, followed by aggregated coded keys in the second round for task recovery. For $\sf{2\leq K_c \leq U-1}$, we use robust symmetric private computation to recover linear combinations of keys in the second round. The objective is to minimize the number of symbols sent by each user during the two rounds. Our proposed schemes have achieved the optimal rate region when $ \sf K_c $ is equal to (1) and the order optimal rate (within 2) when $\sf{2\leq K_c \leq U-1}$.

cs.IT↗

arXiv2025

SQ-GAN: Semantic Image Communications Using Masked Vector Quantization

This work introduces Semantically Masked Vector Quantized Generative Adversarial Network (SQ-GAN), a novel approach integrating semantically driven image coding and vector quantization to optimize image compression for semantic/task-oriented communications. The method only acts on source coding and is fully compliant with legacy systems. The semantics is extracted from the image computing its semantic segmentation map using off-the-shelf software. A new specifically developed semantic-conditioned adaptive mask module (SAMM) selectively encodes semantically relevant features of the image. The relevance of the different semantic classes is task-specific, and it is incorporated in the training phase by introducing appropriate weights in the loss function. SQ-GAN outperforms state-of-the-art image compression schemes such as JPEG2000, BPG, and deep-learning based methods across multiple metrics, including perceptual quality and semantic segmentation accuracy on the reconstructed image, at extremely low compression rates.

cs.CV↗

arXiv2025

Robustness of Covariance Estimators with Application in Activity Detection

The first part of this work considers a general class of covariance estimators. Each estimator of that class is generated by a real-valued function $g$ and a set of model covariance matrices $H$. If $\bf{W}$ is a potentially perturbed observation of a searched covariance matrix, then the estimator is the minimizer of the sum of $g$ applied to each eigenvalue of $\bf{W}^\frac{1}{2}\bf{Z}^{-1}\bf{W}^\frac{1}{2}$ under the constraint that $\bf{Z}$ is from $H$. It is shown that under mild conditions on $g$ and $H$ such estimators are robust, meaning the estimation error can be made arbitrarily small if the perturbation of $\bf{W}$ gets small enough. \par In the second part of this work the previous results are applied to activity detection in random access with multiple receive antennas. In activity detection recovering the large scale fading coefficients is a sparse recovery problem which can be reduced to a structured covariance estimation problem. The recovery can be done with a non-negative least squares estimator or with a relaxed maximum likelihood estimator. It is shown that under suitable assumptions on the distributions of the noise and the channel coefficients, the relaxed maximum likelihood estimator is from the general class of covariance estimators considered in the first part of this work. Then, codebooks based upon a signed kernel condition are proposed. It is shown that with the proposed codebooks both estimators can recover the large-scale fading coefficients if the number of receive antennas is high enough and $S\leq\left\lceil\frac{1}{2}M^2\right\rceil-1$ where $S$ is the number of active users and $M$ is number of pilot symbols per user.

cs.IT↗

arXiv2025

Optimizing Fronthaul Quantization for Flexible User Load in Cell-Free Massive MIMO

We investigate the physical layer (PHY) spectral efficiency and fronthaul network load of a scalable user-centric cell-free massive MIMO system. Each user-centric cluster processor responsible for cluster-level signal processing is located at one of multiple decentralized units (DUs). Thus, the radio units in the cluster must exchange data with the corresponding DU over the fronthaul. Because the fronthaul links have limited capacity, this data must be quantized before it is sent over the fronthaul. We consider a routed fronthaul network, where the cluster processor placement and fronthaul traffic routing are jointly optimized with a mixed-integer linear program. For different numbers of users in the network, we investigate the effect of fronthaul quantization rates, a system parameter computed based on rate-distortion theory. Our results show that with optimized quantization rates, the fronthaul load is quite stable for a wide range of user loads without significant PHY performance loss. This demonstrates that the cell-free massive MIMO PHY and fronthaul network are resilient to varying user densities.

cs.IT↗

arXiv2025

Sparse Regression LDPC Codes for the Block-Fading Non-Coherent SIMO Channel

Sparse regression codes (SPARCs) are a class of codes that encode information through the superposition of columns of a randomised coding matrix. The combination with an outer non-binary low density parity check (NB-LDPC) code was recently shown to improve the finite-length performance of these codes over the unfaded AWGN channel. In this paper, we propose a low-complexity approximate message passing (AMP) decoder that is capable of decoding NB-LDPC encoded SPARCs on a Rayleigh fading channel with multiple receive antennas. Notably, the decoder does not require channel state information (CSI), i.e., it is fully non-coherent, but achieves the same error probability as a decoder with full CSI, even for moderate block lengths. This is achieved by iteratively re-estimating the channel throughout the decoding iterations. In addition, we provide a rigorous asymptotic analysis of both the block error probability and the channel estimation error. Numerical results confirm the precision of the analysis and show that the presented coding scheme performs within 1.5 dB of the outage capacity and is competitive with coded modulation schemes employing standardised LDPC codes for 5G cellular networks and pilot-based channel estimation.

cs.IT↗