arXiv subjects

Italo Atzeni

Publications and source records attributed to Italo Atzeni.

At least 37 records · Page 2Linked to original sources

arXiv2025

Wavenumber-Division Multiplexing in Holographic MIMO with NLoS Channels

Wavenumber-division multiplexing (WDM) was introduced as a counterpart of orthogonal frequency-division multiplexing in the spatial-frequency domain for line-of-sight holographic multiple-input multiple-output (MIMO) systems. In this paper, we extend WDM to holographic MIMO channels with non-line-of-sight (NLoS) propagation. We show that applying WDM to the NLoS channel yields the corresponding angular-domain representation, which we characterize through the power spectral factor and power spectral density. We further obtain a closed-form characterization for the case of isotropic scattering, recovering Jakes' isotropic model. The analysis is complemented by numerical results evaluating the degrees of freedom and ergodic capacity under both isotropic and non-isotropic scattering.

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arXiv2025

SEP Analysis of 1-Bit Quantized SIMO Systems with QPSK over Fading Channels

The average symbol error probability (SEP) of a 1-bit quantized single-input multiple-output (SIMO) system is analyzed under Rayleigh fading channels and quadrature phase-shift keying (QPSK) modulation. Previous studies have partially characterized the diversity gain for selection combining (SC). In this paper, leveraging a novel analytical method, an exact analytical SEP expression is derived for a 1-bit quantized SIMO system employing QPSK modulation at the transmitter and maximum ratio combining (MRC) at the receiver. The corresponding diversity and coding gains of a SIMO-MRC system are also determined. Furthermore, the diversity and coding gains of a 1-bit quantized SIMO-SC system are quantified for an arbitrary number of receive antennas, thereby extending and complementing prior results.

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arXiv2025

Massive MIMO with 1-Bit DACs: Data Detection for Quantized Linear Precoding with Dithering

To leverage high-frequency bands in 6G wireless systems and beyond, employing massive multiple-input multipleoutput (MIMO) arrays at the transmitter and/or receiver side is crucial. To mitigate the power consumption and hardware complexity across massive frequency bands and antenna arrays, a sacrifice in the resolution of the data converters will be inevitable. In this paper, we consider a point-to-point massive MIMO system with 1-bit digital-to-analog converters at the transmitter, where the linearly precoded signal is supplemented with dithering before the 1-bit quantization. For this system, we propose a new maximumlikelihood (ML) data detection method at the receiver by deriving the mean and covariance matrix of the received signal, where symbol-dependent linear minimum mean squared error estimation is utilized to efficiently linearize the transmitted signal. Numerical results show that the proposed ML method can provide gains of more than two orders of magnitude in terms of symbol error rate over conventional data detection based on soft estimation.

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arXiv2025

MIMO Detection with Spatial Sigma-Delta ADCs: A Variational Bayesian Approach

The spatial Sigma-Delta ($ΣΔ$) architecture can be leveraged to reduce the quantization noise and enhance the effective resolution of few-bit analog-to-digital converters (ADCs) at certain spatial frequencies of interest. Utilizing the variational Bayesian (VB) inference framework, this paper develops novel data detection algorithms tailored for massive multiple-input multiple-output (MIMO) systems with few-bit $ΣΔ$ ADCs and angular channel models, where uplink signals are confined to a specific angular sector. We start by modeling the corresponding Bayesian networks for the $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order $ΣΔ$ receivers. Next, we propose an iterative algorithm, referred to as Sigma-Delta variational Bayes (SD-VB), for MIMO detection, offering low-complexity updates through closed-form expressions of the variational densities of the latent variables. We also study the impact of mutual coupling on the performance of the proposed SD-VB algorithms when the antenna spacing is reduced. Simulation results show that the proposed $2^{\mathrm{nd}}$-order SD-VB algorithm delivers the best symbol error rate (SER) performance while maintaining the same computational complexity as in unquantized systems, matched-filtering VB with conventional quantization, and linear minimum mean-squared error (LMMSE) methods. Moreover, the $1^{\mathrm{st}}$- and $2^{\mathrm{nd}}$-order SD-VB algorithms achieve their lowest SER at an antenna separation of one-fourth wavelength for a fixed number of antenna elements. The effects of mutual coupling, the steering angle of the $ΣΔ$ architecture, the number of ADC resolution bits, and the number of antennas and users are also extensively analyzed.

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arXiv2025

6G Resilience -- White Paper

6G must be designed to withstand, adapt to, and evolve amid prolonged, complex disruptions. Mobile networks' shift from efficiency-first to sustainability-aware has motivated this white paper to assert that resilience is a primary design goal, alongside sustainability and efficiency, encompassing technology, architecture, and economics. We promote resilience by analysing dependencies between mobile networks and other critical systems, such as energy, transport, and emergency services, and illustrate how cascading failures spread through infrastructures. We formalise resilience using the 3R framework: reliability, robustness, resilience. Subsequently, we translate this into measurable capabilities: graceful degradation, situational awareness, rapid reconfiguration, and learning-driven improvement and recovery. Architecturally, we promote edge-native and locality-aware designs, open interfaces, and programmability to enable islanded operations, fallback modes, and multi-layer diversity (radio, compute, energy, timing). Key enablers include AI-native control loops with verifiable behaviour, zero-trust security rooted in hardware and supply-chain integrity, and networking techniques that prioritise critical traffic, time-sensitive flows, and inter-domain coordination. Resilience also has a techno-economic aspect: open platforms and high-quality complementors generate ecosystem externalities that enhance resilience while opening new markets. We identify nine business-model groups and several patterns aligned with the 3R objectives, and we outline governance and standardisation. This white paper serves as an initial step and catalyst for 6G resilience. It aims to inspire researchers, professionals, government officials, and the public, providing them with the essential components to understand and shape the development of 6G resilience.

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arXiv2025

Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems operating at sub-THz carrier frequencies represent a promising solution to meet the demands of next-generation wireless applications. This work focuses on sparse channel estimation for XL-MIMO systems operating in the near-field (NF) regime. Assuming a practical subarray-based architecture, we develop a NF channel estimation framework based on adaptive filtering, referred to as \textit{polar-domain zero-attracting least mean squares (PD-ZALMS)}. The proposed method achieves significantly superior channel estimation accuracy and lower computational complexity compared with the well-established polar-domain orthogonal matching pursuit. In addition, the proposed PD-ZALMS is shown to outperform the oracle least-squares channel estimator at low-to-moderate signal-to-noise ratio.

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arXiv2025

A New Framework for the Sum of Squared $κ$-$μ$ RVs with Application to Sub-THz Systems

In this paper, we adopt the $κ$-$μ$ model to characterize the propagation in the sub-THz band. We develop a new exact representation of the sum of squared independent and identically distributed $κ$-$μ$ random variables, which can be used to express the power of the received signal in multi-antenna systems. Unlike existing ones, the proposed analytical framework is remarkably tractable and computationally efficient, and thus can be conveniently employed to analyze systems with massive antenna arrays. We derive novel expressions for the probability density function and cumulative distribution function, analyze their convergence and truncation error, and discuss the computational complexity and the implementation aspects. Moreover, we derive expressions for the coverage probability and bit error probability for coherent binary modulations. Lastly, we evaluate the performance of an uplink sub-THz system where a single-antenna user is served by a base station employing maximum ratio combining.

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arXiv2025

Joint Beamforming Design and Bit Allocation in Massive MIMO with Resolution-Adaptive ADCs

Low-resolution analog-to-digital converters (ADCs) have emerged as a promising technology for reducing power consumption and complexity in massive multiple-input multiple-output (MIMO) systems while maintaining satisfactory spectral and energy efficiencies (SE/EE). In this work, we first identify the essential properties of optimal quantization and leverage them to derive a closed-form approximation of the covariance matrix of the quantization distortion. The theoretical finding facilitates the system SE analysis in the presence of low-resolution ADCs. We then focus on the joint optimization of the transmit-receive beamforming and bit allocation to maximize the SE under constraints on the transmit power and the total number of active ADC bits. To solve the resulting mixed-integer problem, we first develop an efficient beamforming design for fixed ADC resolutions. Then, we propose a low-complexity heuristic algorithm to iteratively optimize the ADC resolutions and beamforming matrices. Numerical results for a $64 \times 64$ MIMO system demonstrate that the proposed design offers $6\%$ improvement in both SE and EE with $40\%$ fewer active ADC bits compared with the uniform bit allocation. Furthermore, we numerically show that receiving more data streams with low-resolution ADCs can achieve higher SE and EE compared to receiving fewer data streams with high-resolution ADCs.

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arXiv2025

Digital and Hybrid Precoding Designs in Massive MIMO with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have emerged as an efficient solution for massive multiple-input multiple-output (MIMO) systems to reap high data rates with reasonable power consumption and hardware complexity. In this paper, we study precoding designs for digital, fully connected (FC) hybrid, and partially connected (PC) hybrid beamforming architectures in massive MIMO systems with low-resolution ADCs at the receiver. We aim to maximize the spectral efficiency (SE) subject to a transmit power budget and hardware constraints on the analog components. The resulting problems are nonconvex and the quantization distortion introduces additional challenges. To address them, we first derive a tight lower bound for the SE, based on which we optimize the precoders for the three beamforming architectures under the majorization-minorization framework. Numerical results validate the superiority of the proposed precoding designs over their state-of-the-art counterparts in systems with low-resolution ADCs, particularly those with 1-bit resolution. The results show that the PC hybrid precoding design can achieve an SE close to those of the digital and FC hybrid precoding designs in 1-bit systems, highlighting the potential of the PC hybrid beamforming architectures.

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arXiv2025

Sum of Squared Extended $η$-$μ$ and $κ$-$μ$ RVs: A New Framework Applied to FR3 and Sub-THz Systems

The analysis of systems operating in future frequency ranges calls for a proper statistical channel characterization through generalized fading models. In this paper, we adopt the Extended $η$-$μ$ and $κ$-$μ$ models to characterize the propagation in FR3 and the sub-THz band, respectively. For these models, we develop a new exact representation of the sum of squared independent and identically distributed random variables, which can be used to express the power of the received signal in multi-antenna systems. Unlike existing ones, the proposed analytical framework is remarkably tractable and computationally efficient, and thus can be conveniently employed to analyze systems with massive antenna arrays. For both the Extended $η$-$μ$ and $κ$-$μ$ distributions, we derive novel expressions for the probability density function and cumulative distribution function, we analyze their convergence and truncation error, and we discuss the computational complexity and implementation aspects. Moreover, we derive expressions for the outage and coverage probability, bit error probability for coherent binary modulations, and symbol error probability for M-ary phase-shift keying and quadrature amplitude modulation. Lastly, we provide an extensive performance evaluation of FR3 and sub-THz systems focusing on a downlink scenario where a single-antenna user is served by a base station employing maximum ratio transmission.

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arXiv2025

On Optimal MMSE Channel Estimation for One-Bit Quantized MIMO Systems

This paper focuses on the minimum mean squared error (MMSE) channel estimator for multiple-input multiple-output (MIMO) systems with one-bit quantization at the receiver side. Despite its optimality and significance in estimation theory, the MMSE channel estimator has not been fully investigated in this context due to its general non-linearity and computational complexity. Instead, the typically suboptimal Bussgang linear MMSE (BLMMSE) estimator has been widely adopted. In this work, we develop a new framework to compute the MMSE channel estimator that hinges on computation of the orthant probability of the multivariate normal distribution. Based on this framework, we determine a necessary and sufficient condition for the BLMMSE channel estimator to be optimal and equivalent to the MMSE estimator. Under the assumption of specific channel correlation or pilot symbols, we further utilize the framework to derive analytical expressions for the MMSE channel estimator that are particularly convenient for computation when certain system dimensions become large, thereby enabling a comparison between the BLMMSE and MMSE channel estimators in these cases.

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arXiv2025

Hybrid Receiver Design for Massive MIMO-OFDM with Low-Resolution ADCs and Oversampling

Low-resolution analog-to-digital converters (ADCs) and hybrid beamforming have emerged as efficient solutions to reduce power consumption with satisfactory spectral efficiency (SE) in massive multiple-input multiple-output (MIMO) systems. In this paper, we investigate the performance of a hybrid receiver in massive MIMO orthogonal frequency-division multiplexing (OFDM) uplink systems with low-resolution ADCs and oversampling. Considering both the temporal and spatial correlation of the quantization distortion (QD), we derive a closed-form approximation of the frequency-domain QD covariance matrix, which facilitates the evaluation of the system's SE. Then we jointly design the analog and digital combiners of the hybrid receiver to maximize the SE. The formulated problem is challenging due to the constant-modulus constraint of the analog combiner and its coupling with the digital one. To overcome these challenges, we transform the objective function into an equivalent but more tractable form and then iteratively update the analog and digital combiners. Numerical simulations verify the superiority of the proposed algorithm over the considered benchmarks and show the resilience of the hybrid receiver to beam squint with low-resolution ADCs. Furthermore, the proposed hybrid receiver design with oversampling can achieve significantly higher energy efficiency compared with the fully digital one.

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arXiv2025

Data-Aided Regularization of Direct-Estimate Combiner in Distributed MIMO Systems

This paper explores the data-aided regularization of the direct-estimate combiner in the uplink of a distributed multiple-input multiple-output system. The network-wide combiner can be computed directly from the pilot signal received at each access point, eliminating the need for explicit channel estimation. However, the sample covariance matrix of the received pilot signal that is used in its computation may significantly deviate from the actual covariance matrix when the number of pilot symbols is limited. To address this, we apply a regularization to the sample covariance matrix using a shrinkage coefficient based on the received data signal. Initially, the shrinkage coefficient is determined by minimizing the difference between the sample covariance matrices obtained from the received pilot and data signals. Given the limitations of this approach in interference-limited scenarios, the shrinkage coefficient is iteratively optimized using the sample mean squared error of the hard-decision symbols, which is more closely related to the actual system's performance, e.g., the symbol error rate (SER). Numerical results demonstrate that the proposed regularization of the direct-estimate combiner significantly enhances the SER, particularly when the number of pilot symbols is limited.

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arXiv2025

Enhanced Sparse Bayesian Learning Methods with Application to Massive MIMO Channel Estimation

We consider the problem of sparse channel estimation in massive multiple-input multiple-output systems. In this context, we propose an enhanced version of the sparse Bayesian learning (SBL) framework, referred to as enhanced SBL (E-SBL), which is based on a reparameterization of the original SBL model. Specifically, we introduce a scale vector that brings extra flexibility to the model, which is estimated along with the other unknowns. Moreover, we introduce a variant of E-SBL, referred to as modified E-SBL (M-E-SBL), which is based on a computationally more efficient parameter estimation. We compare the proposed E-SBL and M-E-SBL with the baseline SBL and with a method based on variational message passing (VMP) in terms of computational complexity and performance. Numerical results show that the proposed E-SBL and M-E-SBL outperform the baseline SBL and VMP in terms of mean squared error of the channel estimation in all the considered scenarios. Furthermore, we show that M-E-SBL produces results comparable with E-SBL with considerably cheaper computations.

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arXiv2024

Analysis of Oversampling in Uplink Massive MIMO-OFDM with Low-Resolution ADCs

Low-resolution analog-to-digital converters (ADCs) have emerged as an efficient solution for massive multiple-input multiple-output (MIMO) systems to reap high data rates with reasonable power consumption and hardware complexity. In this paper, we analyze the performance of oversampling in uplink massive MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems with low-resolution ADCs. Considering both the temporal and spatial correlation of the quantization distortion, we derive an approximate closed-form expression of an achievable sum rate, which reveals how the oversampling ratio (OSR), the ADC resolution, and the signal-to-noise ratio (SNR) jointly affect the system performance. In particular, we demonstrate that oversampling can effectively improve the sum rate by mitigating the impact of the quantization distortion, especially at high SNR and with very low ADC resolution. Furthermore, we show that the considered low-resolution massive MIMO-OFDM system can achieve the same performance as the unquantized one when both the SNR and the OSR are sufficiently high. Numerical simulations confirm our analysis.

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arXiv2024

Uplink Transmit Power Optimization for Distributed Massive MIMO Systems with 1-Bit ADCs

This paper addresses the problem of uplink transmit power optimization in distributed massive multiple-input multiple-output systems, where remote radio heads (RRHs) are equipped with 1-bit analog-to-digital converters (ADCs). First, in a scenario where a single RRH serves a single user equipment (UE), the signal-to-noise-and-distortion ratio (SNDR) is shown to be a non-monotonic and unimodal function of the UE transmit power due to the quantization distortion (QD). Upon the introduction of multiple RRHs, adding properly tuned dithering at each RRH is shown to render the SNDR at the output of the joint receiver unimodal. In a scenario with multiple RRHs and UEs, considering the non-monotonic nature of the signal-to-interference-plus-noise-and-distortion ratio (SINDR), both the UE transmit powers and the RRH dithering levels are jointly optimized subject to the min-power and max-min-SINDR criteria, while employing Bussgang-based maximum ratio combining (BMRC) and minimum mean squared error (BMMSE) receivers. To this end, gradient and block coordinate descent methods are introduced to tune the UE transmit powers, whereas a line search coupled with gradient updates is used to adjust the RRH dithering levels. Numerical results demonstrate that jointly optimizing the UE transmit power and the RRH dithering levels can significantly enhance the system performance, thus facilitating joint reception from multiple RRHs across a range of scenarios. Comparing the BMMSE and BMRC receivers, the former offers a better interference and QD alleviation while the latter has a lower computational complexity.

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arXiv2024

Optimality of the Bussgang Linear MMSE Channel Estimator for MIMO Systems with 1-Bit ADCs

In this paper, we study the optimality of the Bussgang linear minimum mean squared error (BLMMSE) channel estimator for multiple-input multiple-output systems with 1-bit analog-to-digital converters. We compare the BLMMSE with the optimal minimum mean squared error (MMSE) channel estimator, which is generally non-linear, and we develop a novel framework based on the orthant probability of a multivariate normal distribution to compute the MMSE channel estimate. Then, we analyze the equivalence of the MMSE and BLMMSE channel estimators under specific assumptions on the channel correlation or pilot symbols. Interestingly, the BLMMSE channel estimator turns out to be optimal in several specific cases. Our study culminates with the presentation of a necessary and sufficient condition for the BLMMSE channel estimator to be optimal.

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arXiv2024

Combined DL-UL Distributed Beamforming Design for Cell-Free Massive MIMO

We consider a cell-free massive multiple-input multiple-output system with multi-antenna access points (APs) and user equipments (UEs), where the UEs can be served in both the downlink (DL) and uplink (UL) within a resource block. We tackle the combined optimization of the DL precoders and combiners at the APs and DL UEs, respectively, together with the UL combiners and precoders at the APs and UL UEs, respectively. To this end, we propose distributed beamforming designs enabled by iterative bi-directional training (IBT) and based on the minimum mean squared error criterion. To reduce the IBT overhead and thus enhance the effective DL and UL rates, we carry out the distributed beamforming design by assuming that all the UEs are served solely in the DL and then utilize the obtained beamformers for the DL and UL data transmissions after proper scaling. Numerical results show the superiority of the proposed combined DL-UL distributed beamforming design over separate DL and UL designs, especially with short resource blocks.

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