arXiv subjects

Giuseppe Caire

Publications and source records attributed to Giuseppe Caire.

At least 145 records · Page 8Linked to original sources

arXiv2024

Cooperative Multistatic Target Detection in Cell-Free Communication Networks

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.

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arXiv2024

Joint Antenna Selection and Covariance Matrix Optimization for ISAC Systems

We consider an integrated sensing and communication (ISAC) system with a single communication user and multiple targets. For the communication functionality, the achievable rate is employed as the performance metric, while for sensing, we focus on minimizing the mean squared error (MSE) between the designed beampattern and a desired one for tracking the targets. Towards this, and by assuming that there are fewer radiofrequency (RF) chains than antenna elements at the transmitter (Tx), we focus on the joint antenna selection (AS) and covariance matrix (CM) optimization at the Tx. This is a mixed-integer optimization problem, yet we demonstrate that it can be efficiently solved, in polynomial time, by combining convex optimization tools with dynamic programming (DP). By introducing an adjustable trade-off parameter, we formulate a joint objective function that captures both the communication and sensing metric. In this way, different ISAC solutions can be obtained, considering the trade-off among the two functionalities. It is shown that selecting the active antennas with our proposed method is superior than assuming a uniform Tx array with fixed antenna positions. Notably, by individually considering the optimization of either the sensing or the communication system alone, our proposed algorithm outperforms the literature proposals, by incurring only a small increase in complexity.

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arXiv2024

A Semantic Model for Physical Layer Deception

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

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arXiv2024

Analysis of Cross-Domain Message Passing for OTFS Transmissions

In this paper, we investigate the performance of the cross-domain iterative detection (CDID) framework with orthogonal time frequency space (OTFS) modulation, where two distinct CDID algorithms are presented. The proposed schemes estimate/detect the information symbols iteratively across the frequency domain and the delay-Doppler (DD) domain via passing either the a posteriori or extrinsic information. Building upon this framework, we investigate the error performance by considering the bias evolution and state evolution. Furthermore, we discuss their error performance in convergence and the DD domain error state lower bounds in each iteration. Specifically, we demonstrate that in convergence, the ultimate error performance of the CDID passing the a posteriori information can be characterized by two potential convergence points. In contrast, the ultimate error performance of the CDID passing the extrinsic information has only one convergence point, which, interestingly, aligns with the matched filter bound. Our numerical results confirm our analytical findings and unveil the promising error performance achieved by the proposed designs.

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arXiv2024

Dataset of Pathloss and ToA Radio Maps With Localization Application

In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available. The datasets include simulated pathloss/received signal strength (RSS) and time of arrival (ToA) radio maps over a large collection of realistic dense urban setting in real city maps. The two main applications of the presented dataset are 1) learning methods that predict the pathloss from input city maps (namely, deep learning-based simulations), and, 2) wireless localization. The fact that the RSS and ToA maps are computed by the same simulations over the same city maps allows for a fair comparison of the RSS and ToA-based localization methods.

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arXiv2024

Fundamental Limits of Multi-Message Private Computation

In a typical formulation of the private information retrieval (PIR) problem, a single user wishes to retrieve one out of $ K$ files from $N$ servers without revealing the demanded file index to any server. This paper formulates an extended model of PIR, referred to as multi-message private computation (MM-PC), where instead of retrieving a single file, the user wishes to retrieve $P>1$ linear combinations of files while preserving the privacy of the demand information. The MM-PC problem is a generalization of the private computation (PC) problem (where the user requests one linear combination of the files), and the multi-message private information retrieval (MM-PIR) problem (where the user requests $P>1$ files). A baseline achievable scheme repeats the optimal PC scheme by Sun and Jafar $P$ times, or treats each possible demanded linear combination as an independent file and then uses the near optimal MM-PIR scheme by Banawan and Ulukus. In this paper, we propose a new MM-PC scheme that significantly improves upon the baseline schemes. In doing so, we design the queries inspired by the structure in the cache-aided scalar linear function retrieval scheme by Wan {\it et al.}, which leverages the dependency between linear functions to reduce the amount of communications. To ensure the decodability of our scheme, we propose a new method to benefit from the existing dependency, referred to as the sign assignment step. In the end, we use Maximum Distance Separable matrices to code the queries, which allows the reduction of download from the servers, while preserving privacy. By the proposed schemes, we characterize the capacity within a multiplicative factor of $2$.

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arXiv2024

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

We consider unsourced random access (uRA) in user-centric cell-free (CF) wireless networks, where random access users send codewords from a common codebook during specifically dedicated random access channel (RACH) slots. The system is conceptually similar to the so-called 2-step RACH currently discussed in 3GPP standardization. In order to cope with the distributed and CF nature of the network, we propose to partition the network coverage area into zones (referred to as ''locations'') and assign an uRA codebook to each location, such that users in a certain location make use of the associated codebook. The centralized uRA decoder makes use of the multisource AMP algorithm recently proposed by the authors. This yields at once the list of active uRA codewords, an estimate of the corresponding channel vectors, and an estimate of the active users' position. We show excellent performance of this approach and perfect agreement with the rigorous theoretical ''state evolution'' analysis. We also show that the proposed ''location-based'' partitioned codebook approach significantly outperforms a baseline system with a single non-partitioned uRA codebook.

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arXiv2024

A Modular Pragmatic Architecture for Multiuser MIMO with Array-Fed RIS

We propose a power- and hardware-efficient, pragmatic, modular, multiuser/multibeam array-fed RIS architecture particularly suited to operate in very high frequency bands (high mmWave and sub-THz), where channels are typically sparse in the beamspace and line-of-sight (LOS) is required to achieve an acceptable received signal level. The key module is an active multi-antenna feeder (AMAF) with a small number of active antennas placed in the near field of a RIS with a much larger number of passive controllable reflecting elements. We propose a pragmatic approach to obtain a steerable beam with high gain and very low sidelobes. Then, $K$ independently controlled beams can be achieved by stacking $K$ of such AMAF-RIS modules. Our analysis takes in full account: 1) the near-end crosstalk (NEXT) between the modules, 2) the far-end crosstalk (FEXT) due to the sidelobes; 3) a thorough energy efficiency comparison with respect to conventional active arrays with the same beamforming performance. Overall, we show that the proposed architecture is very attractive in terms of spectral efficiency, ease of implementation (hardware complexity), and energy efficiency.

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arXiv2024

Joint Fronthaul Load Balancing and Computation Resource Allocation in Cell-Free User-Centric Massive MIMO Networks

We consider scalable cell-free massive multiple-input multiple-output networks under an open radio access network paradigm comprising user equipments (UEs), radio units (RUs), and decentralized processing units (DUs). UEs are served by dynamically allocated user-centric clusters of RUs. The corresponding cluster processors (implementing the physical layer for each user) are hosted by the DUs as software-defined virtual network functions. Unlike the current literature, mainly focused on the characterization of the user rates under unrestricted fronthaul communication and computation, in this work we explicitly take into account the fronthaul topology, the limited fronthaul communication capacity, and computation constraints at the DUs. In particular, we systematically address the new problem of joint fronthaul load balancing and allocation of the computation resource. As a consequence of our new optimization framework, we present representative numerical results highlighting the existence of an optimal number of quantization bits in the analog-to-digital conversion at the RUs.

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arXiv2024

Resource Allocation Design for Next-Generation Multiple Access: A Tutorial Overview

Multiple access is the cornerstone technology for each generation of wireless cellular networks and resource allocation design plays a crucial role in multiple access. In this paper, we present a comprehensive tutorial overview for junior researchers in this field, aiming to offer a foundational guide for resource allocation design in the context of next-generation multiple access (NGMA). Initially, we identify three types of channels in future wireless cellular networks over which NGMA will be implemented, namely: natural channels, reconfigurable channels, and functional channels. Natural channels are traditional uplink and downlink communication channels; reconfigurable channels are defined as channels that can be proactively reshaped via emerging platforms or techniques, such as intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and movable/fluid antenna (M/FA); and functional channels support not only communication but also other functionalities simultaneously, with typical examples including integrated sensing and communication (ISAC) and joint computing and communication (JCAC) channels. Then, we introduce NGMA models applicable to these three types of channels that cover most of the practical communication scenarios of future wireless communications. Subsequently, we articulate the key optimization technical challenges inherent in the resource allocation design for NGMA, categorizing them into rate-oriented, power-oriented, and reliability-oriented resource allocation designs. The corresponding optimization approaches for solving the formulated resource allocation design problems are then presented. Finally, simulation results are presented and discussed to elucidate the practical implications and insights derived from resource allocation designs in NGMA.

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arXiv2024

A New Achievable Region of the $K$-User MAC Wiretap Channel with Confidential and Open Messages Under Strong Secrecy

This paper investigates the achievable region of a $K$-user discrete memoryless (DM) multiple access wiretap (MAC-WT) channel, where each user transmits both secret and open messages. All these messages are intended for Bob, while Eve is only interested in the secret messages. In the achievable coding strategy, the confidential information is protected by open messages and also by the introduction of auxiliary messages. When introducing an auxiliary message, one has to ensure that, on one hand, its rate is large enough for protecting the secret message from Eve and, on the other hand, the resulting sum rate (together with the secret and open message rate) does not exceed Bob's decoding capability. This yields an inequality structure involving the rates of all users' secret, open, and auxiliary messages. To obtain the rate region, the auxiliary message rates must be eliminated from the system of inequalities. A direct application of the Fourier-Motzkin elimination procedure is elusive since a) it requires that the number of users $K$ is explicitly given, and b) even for small $K = 3, 4, \ldots$, the number of inequalities becomes extremely large. We prove the result for general $K$ through the combined use of Fourier-Motzkin elimination procedure and mathematical induction. This paper adopts the strong secrecy metric, characterized by information leakage. To prove the achievability under this criterion, we analyze the resolvability region of a $K$-user DM-MAC channel. In addition, we show that users with zero secrecy rate can play different roles and use different strategies in encoding their messages. These strategies yield non-redundant rate inequalities. By considering all possible coding strategies, we provide a new achievable region for the considered channel, and show that it strictly improves those already known in the existing literature by considering a specific example.

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arXiv2024

Compressed Sensing Inspired User Acquisition for Downlink Integrated Sensing and Communication Transmissions

This paper investigates radar-assisted user acquisition for downlink multi-user multiple-input multiple-output (MIMO) transmission using Orthogonal Frequency Division Multiplexing (OFDM) signals. Specifically, we formulate a concise mathematical model for the user acquisition problem, where each user is characterized by its delay and beamspace response. Therefore, we propose a two-stage method for user acquisition, where the Multiple Signal Classification (MUSIC) algorithm is adopted for delay estimation, and then a least absolute shrinkage and selection operator (LASSO) is applied for estimating the user response in the beamspace. Furthermore, we also provide a comprehensive performance analysis of the considered problem based on the pair-wise error probability (PEP). Particularly, we show that the rank and the geometric mean of non-zero eigenvalues of the squared beamspace difference matrix determines the user acquisition performance. More importantly, we reveal that simultaneously probing multiple beams outperforms concentrating power on a specific beam direction in each time slot under the power constraint, when only limited OFDM symbols are transmitted. Our numerical results confirm our conclusions and also demonstrate a promising acquisition performance of the proposed two-stage method.

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arXiv2024

Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

Integrated Sensing and Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Hence, to effectively enable ISAC, considerable research has been directed toward addressing the clock asynchronism issue in bi-static sensing. This paper provides an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC.

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arXiv2024

Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address the combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to the upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

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arXiv2024

An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures

In this paper, we study a remote source coding scenario in which binary phase shift keying (BPSK) modulation sources are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs additional compression to balance complexity and relevance. This problem can be further formulated as an information bottleneck (IB) problem with Bernoulli sources and Gaussian mixture observations. However, no closed-form solution exists for this IB problem. To address this challenge, we propose a unified achievable scheme that employs three different compression/quantization strategies for intermediate node processing by using two-level quantization, multi-level deterministic quantization, and soft quantization with the hyperbolic tangent ($\tanh$) function, respectively. In addition, we extend our analysis to the vector mixture Gaussian observation problem and explore its application in machine learning for binary classification with information leakage. Numerical evaluations show that the proposed scheme has a near-optimal performance over various signal-to-noise ratios (SNRs), compared to the Blahut-Arimoto (BA) algorithm, and has better performance than some existing numerical methods such as the information dropout approach. Furthermore, experiments conducted on the realistic MNIST dataset also validate the superior classification accuracy of our method compared to the information dropout approach.

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arXiv2024

Ultra-Dense Cell-Free Massive MIMO for 6G: Technical Overview and Open Questions

Ultra-dense cell-free massive multiple-input multiple-output (CF-MMIMO) has emerged as a promising technology expected to meet the future ubiquitous connectivity requirements and ever-growing data traffic demands in 6G. This article provides a contemporary overview of ultra-dense CF-MMIMO networks, and addresses important unresolved questions on their future deployment. We first present a comprehensive survey of state-of-the-art research on CF-MMIMO and ultra-dense networks. Then, we discuss the key challenges of CF-MMIMO under ultra-dense scenarios such as low-complexity architecture and processing, low-complexity/scalable resource allocation, fronthaul limitation, massive access, synchronization, and channel acquisition. Finally, we answer key open questions, considering different design comparisons and discussing suitable methods dealing with the key challenges of ultra-dense CF-MMIMO. The discussion aims to provide a valuable roadmap for interesting future research directions in this area, facilitating the development of CF-MMIMO MIMO for 6G.

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arXiv2024

Unsourced Random Access in MIMO Quasi-Static Rayleigh Fading Channels with Finite Blocklength

This paper explores the fundamental limits of unsourced random access (URA) with a random and unknown number ${\rm{K}}_a$ of active users in MIMO quasi-static Rayleigh fading channels. First, we derive an upper bound on the probability of incorrectly estimating the number of active users. We prove that it exponentially decays with the number of receive antennas and eventually vanishes, whereas reaches a plateau as the power and blocklength increase. Then, we derive non-asymptotic achievability and converse bounds on the minimum energy-per-bit required by each active user to reliably transmit $J$ bits with blocklength $n$. Numerical results verify the tightness of our bounds, suggesting that they provide benchmarks to evaluate existing schemes. The extra required energy-per-bit due to the uncertainty of the number of active users decreases as $\mathbb{E}[{\rm{K}}_a]$ increases. Compared to random access with individual codebooks, the URA paradigm achieves higher spectral and energy efficiency. Moreover, using codewords distributed on a sphere is shown to outperform the Gaussian random coding scheme in the non-asymptotic regime.

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arXiv2024

Fairness Scheduling in User-Centric Cell-Free Massive MIMO Wireless Networks

We consider a user-centric cell-free massive MIMO wireless network with $L$ remote radio units, each with $M$ antennas, serving $K_{\rm tot}$ user equipments (UEs). Most of the literature considers the regime $LM \gg K_{\rm tot}$, where the $K$ UEs are active on each time-frequency slot, and evaluates the system performance in terms of ergodic rates. In this paper, we take a quite different viewpoint. We observe that the regime of $LM \gg K_{\rm tot}$ corresponds to a lightly loaded system with low sum spectral efficiency (SE). In contrast, in most relevant scenarios, the number of UEs is much larger than the total number of antennas, but users are not all active at the same time. To achieve high sum SE and handle $K_{\rm tot} \gg ML$, users must be scheduled over the time-frequency resource. The number of active users $K_{\rm act} \leq K_{\rm tot}$ must be chosen such that: 1) the network operates close to its maximum SE; 2) the active user set must be chosen dynamically over time in order to enforce fairness in terms of per-user time-averaged throughput rates. The fairness scheduling problem is formulated as the maximization of a concave componentwise non-decreasing network utility function of the per-user rates. Intermittent user activity imposes slot-by-slot coding/decoding which prevents the achievability of ergodic rates. Hence, we model the per-slot service rates using information outage probability. To obtain a tractable problem, we make a decoupling assumption on the CDF of the instantaneous mutual information seen at each UE $k$ receiver. We approximately enforce this condition with a conflict graph that prevents the simultaneous scheduling of users with large pilot contamination and propose an adaptive scheme for instantaneous service rate scheduling. Overall, the proposed dynamic scheduling is robust to system model uncertainties and can be easily implemented in practice.

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