arXiv subjects

Amine Mezghani

Publications and source records attributed to Amine Mezghani.

At least 19 recordsLinked to original sources

arXiv2026

Power Minimization under Quality of Service Constraints for MIMO Systems with a RIS-based Transmitter

This study investigates a virtual multiuser multiple-input multiple-output (MU-MIMO) system with PSK modulation, realized with a reconfigurable intelligent surface (RIS)-based transmitter. The study focuses on minimizing transmit power under quality-of-service (QoS) constraints while addressing the associated computational complexity. A discrete phase-shift RIS model is considered, and the power minimization problem is formulated in two scenarios. First, for QPSK user data, the symbol-error probability (SEP) is adopted as the QoS criterion. Second, for general $M$-PSK modulation, the union-bound SEP (UBSEP) is used to define the QoS constraints. Based on the considered formulations, a partial branch-and-bound (PBB) approach is developed, which improves on full branch-and-bound (FBB) methods in the sense of allowing for favorable complexity performance trade-offs. For the special case of high-resolution RIS, the discrete phase-shift set is approximated by its continuous counterpart, enabling the reformulation of the original problems as constrained optimizations on an oblique manifold, which are solved with reduced computational complexity with the proposed bisection method. Numerical results demonstrate the effectiveness of the proposed approaches in minimizing the transmit power for different SEP requirements and showcase the balance between power efficiency and computational complexity

cs.IT↗

arXiv2026

Information Rates of Approximate Message Passing for Bandlimited Direct-Detection Channels

The capacity of bandlimited direct-detection channels is challenging to compute or approach due to the receiver non-linearity. A generalized vector approximate message passing (GVAMP) detector is designed to achieve high rates at a reasonable level of complexity. The rates increase by using multi-level coding and successive interference cancellation. The methods are applied to fiber-optic channels with intersymbol interference caused by spectrally efficient pulse shapes, chromatic dispersion, and receiver sampling at twice the baud rate. Bipolar modulation operates within 0.26 bits per channel use (bpcu) of the real-alphabet coherent capacity for optically amplified links, reducing the best-known theoretical gap of 1 bpcu. Remarkably, bipolar modulation achieves 6 dB and 3 dB of power gain over unipolar modulation with and without optical amplification, respectively. Simulations with polar-coded modulation confirm the gains. The GVAMP complexity, measured in multiplications per information bit (mpib), is proportional to the number of iterations and to the logarithm of the block length, and is substantially lower than that of other equalizers. For example, a system with 64-ary bipolar modulation and a root-raised cosine pulse with a 1% roll-off factor was simulated over 4 km of optically amplified standard single-mode fiber in the C-band. The GVAMP receiver requires 93 mpib to achieve 5 bpcu at 300 gigabaud.

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arXiv2026

Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model

Accurate massive MIMO channel state information (CSI) acquisition with low pilot overhead is critical in dynamic propagation environments. Exploiting temporal correlation is key to reducing pilot overhead, yet most existing methods often rely on impractical assumptions. The approximate message passing with side information (AMP-SI) algorithm, built upon a birth-death-drift (BDD) model, represents a significant step in this direction. However, its practical deployment is hindered by three major limitations: reliance on i.i.d. Gaussian sensing matrices, need for perfect BDD parameter knowledge, and a statistically approximate treatment of temporal information. To address these limitations, we introduce BDD-VAMP-EM, a fully automated algorithm that relies on the BDD model, vector AMP (VAMP), and expectation-maximization (EM) in a unified framework. Simulations show that BDD-VAMP-EM consistently outperforms existing benchmarks, particularly under model parameter mismatch, confirming its practical viability.

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arXiv2026

Deep Unfolding for SIM-Assisted Multiband MU-MISO Downlink Systems

To improve the efficiency of scarce radio-frequency (RF) resources in next-generation wireless systems, an intelligent transceiver architecture based on stacked intelligent metasurfaces (SIM) has recently emerged, where multiple programmable metasurface layers are cascaded and each layer comprises passive meta-atoms that perform beamforming directly in the wave domain. In parallel, inter-band carrier aggregation enables multi-band transmission with high spectral efficiency. Their integration in multi-band multiuser downlink transmission is challenging because a single SIM phase configuration must remain effective across all subcarriers, while user scheduling and power allocation vary across scheduling intervals. To address these challenges, we propose an alternating-optimization framework that decomposes the joint design into a power-constrained precoder update and a SIM phase update. For the SIM phase subproblem, we develop a physically consistent multi-band deep-unfolding network (MBDU-Net) that unrolls projected-gradient phase updates into a compact trainable architecture. Each stage computes an analytic gradient from the cascaded SIM channel model and learns lightweight parameters, including per-stage step sizes and band-aware scaling, enabling fast convergence. Numerical results for multi-band multiuser downlink scenarios demonstrate reliable convergence and consistent sum-rate gains on unseen channel realizations.

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arXiv2026

Are Stacked Intelligent Metasurfaces (SIMs) Better than Single-layer Reconfigurable Intelligent Surfaces (RISs) for Wideband Multi-user MIMO Communication Systems?

Cascaded or stacked intelligent metasurfaces (SIMs) have emerged as a promising technology to overcome the physical limitations of single-layer reconfigurable intelligent surfaces (RISs) in wideband wireless communication. By intelligently manipulating electromagnetic waves, SIMs enhance signal propagation in complex environments and offer additional degrees of freedom for beamforming. This paper proposes a coupling-aware, wideband, circuit-based framework that captures frequency-dependent mutual coupling and wideband channel responses over multiple subbands. Based on this model, we formulate a joint active and passive beamforming design that optimizes the base-station precoder to enable carrier aggregation across frequency-selective subbands, together with metasurface phase shifts, to maximize spectral efficiency. Simulation results reveal the importance of accounting for coupling and wideband effects, and show that performance depends strongly on operating conditions. Single-layer RIS configurations can be favorable in narrowband and/or low-SNR regimes, whereas SIMs can significantly outperform under wideband multi-user conditions by mitigating coupling-induced distortion and maintaining a more consistent phase response across frequencies. The results provide physical insights into design trade-offs between structural simplicity and wideband adaptability, highlighting SIMs as a scalable solution for future-generation wideband multi-user MIMO systems. We further show that partially reconfigurable SIM architectures achieve near-optimal performance with reduced complexity.

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arXiv2026

Guided Wireless Technology for Near-Field Communication

Guided wireless technology is an innovative approach that combines the strengths of guided waves and wireless communication. In traditional wireless systems, signals propagate through the air, where they are vulnerable to interference, attenuation, and jamming. Guided communication, in contrast, confines signals within a physical medium, significantly reducing interference and supporting higher data rates over longer distances. Guided wireless technology harnesses these benefits by creating guided wireless channels and offering a controlled pathway for electromagnetic waves. This work harnesses these benefits by focusing on the modeling of near-field communication through long connected arrays deployed in linear-cell environments. We derive a circuit model for long array as an infinitely long dipole with multiple periodic feed points before approximating it with a finite array through open circuiting. Through our simulations, we show how the standing wave phenomenon is confirmed by the oscillations in spectral efficiency. We also demonstrate the capability of the LMMSE transmit beamformer in mitigating interference and minimizing the mean square error by adaptively allocating more power to the user experiencing the most severe channel attenuation, resulting in a more balanced variation of achievable rates across users.

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arXiv2026

Joint Communication and Sensing in RIS-Assisted MIMO System Under Mutual Coupling

This paper considers a downlink Reconfigurable Intelligent Surface (RIS)-assisted Joint Communication and Sensing (JCAS) system within a physically-consistent setting, accounting for the effect of mutual coupling between RIS elements arising due to sub-element spacing. The system features a multiple-input multiple-output (MIMO) terrestrial base station (BS) and explores both monostatic and bistatic radar configurations to enable joint communication and sensing. In the monostatic configuration, both the transmitter and receiver are at the same location, while the bistatic configuration separates the transmitter and receiver spatially. System performance is evaluated using Fisher Information (FI) to quantify sensing accuracy and Mutual Information (MI) to measure communication efficiency. To achieve an optimal balance between communication and sensing, the RIS reflective coefficients and BS transmit beamforming are jointly optimized by maximizing a weighted sum of FI and MI. A novel solution approach is proposed for a single-user, single-object scenario, leveraging the mutual coupling model to enhance system realism. The impact of self-interference on sensing performance is also investigated through signal quantization. Numerical results reveal a fundamental trade-off between FI and MI and demonstrate that incorporating mutual coupling within a physically-consistent framework significantly improves both communication and sensing performance compared to conventional RIS-assisted JCAS models. Additionally, the analysis highlights how the choice of monostatic versus bistatic radar configuration affects system performance, offering valuable insights for the design of RIS-assisted JCAS systems.

cs.NI↗

arXiv2026

Multi-Objective Optimization for Joint Communication and Sensing in Multi-user MIMO Systems: Characterizing the Pareto Boundary

This paper investigates the Pareto boundary performance of a joint communication and sensing (JCAS) system that addresses both sensing and communication functions at the same time. In this scenario, a multiple-antenna base station (BS) transmits information to multiple single-antenna communication users while concurrently estimating the parameters of a single sensing object using the echo signal. We present an integrated beamforming approach for JCAS in a multi-user multiple-input and multiple-output (MIMO) system. The performance measures for communication and sensing are Fisher information (FI) and mutual information (MI). Our research considers two scenarios: multiple communication users with a single sensing object and a single communication user with a single sensing object. We formulate a multi-objective optimization problem to maximize the weighted sum of MI and FI, subject to a total transmit power budget for both cases. As a particular case, we address the equivalent isotropic radiated power (EIRP) for the single communication user scenario. We use the uplink-downlink duality for the multi-user case to simplify the problem and apply Lagrangian optimization and line search methods with a block-coordinate ascending technique. We use projected gradient descent (PGD) to solve the optimization problem in the single-user case. Our numerical results demonstrate that joint beamforming is optimal for the multi-user JCAS system, as opposed to independent beamforming for each user and the sensing object. Furthermore, we reveal the Pareto boundary for the multi-user case, with variations in the number of communication users and the number of transmitting and receiving antennas. We provide the Pareto boundary depending on EIRP limitations for the single-user case.

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arXiv2025

Dual-Function Beam Pattern Design for Multi-Target ISAC Systems: A Decoupled Approach

We investigate the beampattern design problem for mono-static multi-user (MU) multi-point-target integrated sensing and communication (ISAC) systems, where a dual-function multiple-input multiple-output (DF-MIMO) base station (BS) performs downlink communication and radar sensing simultaneously. In ISAC systems, sensing and communication inherently compete for resources. As communication demand increases, the beam pattern is reshaped, which might degrade the direction of arrival (DoA) sensing accuracy, measured in terms of mean-squared error (MSE) and lower-bounded by the Cramer-Rao lower bound (CRLB). Since conventional joint formulations of the sensing-based problem often overlook this trade-off, our work addresses it by decomposing the sensing-based problem into two subproblems (SPs). This decomposition enables a more effective exploitation of the beam pattern's physical properties, which we refer to as the Sensing-Guided Communication Dual-Function (SGCDF) beam pattern design. We further develop a low-complexity extension using the Riemannian Manifold Optimization (RMO) and convex closed-set projection. Simulation results confirm that the proposed method improves multi-target estimation accuracy, compared to traditional joint optimization strategies, by preserving the beam pattern, while the low-complexity version offers an excellent performance-complexity tradeoff, maintaining high accuracy with significantly reduced computational cost.

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arXiv2025

Information Theoretic Analysis of a Dual-Band MIMO Cellphone Antenna with ANSYS HFSS SBR+

Historically, the design of antenna arrays has evolved separately from Shannon theory. Shannon theory adopts a probabilistic approach in the design of communication systems, while antenna design approaches have relied on the deterministic Maxwell theory alone. In this paper, we investigate an information-theoretic analysis approach which we apply to evaluate the design of a dual-band, dual-polarized multiple-input multiple-output (MIMO) array on a cellphone. To this end, we use ANSYS HFSS, a commercial electromagnetic (EM) simulation software suitable for the numerical optimization of antenna systems. HFSS is used to obtain an accurate model of the cellphone MIMO antenna array and HFSS SBR+ is utilized to obtain channel matrices for a large number of users. Taking advantage of linear and optimal processing at the cellphone, we estimate the outage probability curves. The curves are then used to determine the diversity gain in a moderate signal-to-noise ratio (SNR) regime and the multiplexing gain at a high SNR regime. This approach is then compared with the method of estimating the diversity gain from the envelope correlation coefficients or the beam-coupling matrix showing substantial differences in the two methodologies.

cs.IT↗

arXiv2025

Capacity Based Design of Slot Array Antennas

Historically, the design of antenna arrays has evolved separately from Shannon theory. Shannon theory adopts a probabilistic approach in the design of communication systems, while antenna design approaches have relied on deterministic Maxwell theory alone. In this paper, we introduce a new approach to the design of antenna arrays based on information theoretic metrics. To this end, we develop a statistical model suitable for the numerical optimization of antenna systems. The model is utilized to obtain the signal-to-noise ratio (SNR), find the optimal power allocation scheme, and establish the associated Shannon capacity. We demonstrate the utility of the new approach on a connected array of slot antennas. To find the impedance matrix of the slot array, we further develop a fast numerical technique based on the analytical form of the spectrum of magnetic current. The utilized spectral approach, albeit its simplicity, shows good match compared with full wave electromagnetic simulation.

stat.AP↗

arXiv2024

Physically-consistent Multi-band Massive MIMO Systems: A Radio Resource Management Model

Massive multiple-input multiple-output (mMIMO) antenna systems and inter-band carrier aggregation (CA)-enabled multi-band communication are two key technologies to achieve very high data rates in beyond fifth generation (B5G) wireless systems. We propose a joint optimization framework for such systems where the mMIMO antenna spacing selection, precoder optimization, optimum sub-carrier selection and optimum power allocation are carried out simultaneously. We harness the bandwidth gain existing in a tightly coupled base station mMIMO antenna system to avoid sophisticated, non-practical antenna systems for multi-band operation. In particular, we analyze a multi-band communication system using a circuit-theoretic model to consider physical characteristics of a tightly coupled antenna array, and formulate a joint optimization problem to maximize the sum-rate. As part of the optimization, we also propose a novel block iterative water-filling-based sub-carrier selection and power allocation optimization algorithm for the multi-band mMIMO system. A novel sub-carrier windowing-based sub-carrier selection scheme is also proposed which considers the physical constraints (hardware limitation) at the mobile user devices. We carryout the optimizations in two ways: (i) to optimize the antenna spacing selection in an offline manner, and (ii) to select antenna elements from a dense array dynamically. Via computer simulations, we illustrate superior bandwidth gains present in the tightly-coupled colinear and rectangular planar antenna arrays, compared to the loosely-coupled or tightly-coupled parallel arrays. We further show the optimum sum-rate performance of the proposed optimization-based framework under various power allocation schemes and various user capability scenarios.

cs.NI↗

arXiv2024

Unlabeled Compressed Sensing from Multiple Measurement Vectors

This paper introduces an algorithmic solution to a broader class of unlabeled sensing problems with multiple measurement vectors (MMV). The goal is to recover an unknown structured signal matrix, $\mathbf{X}$, from its noisy linear observation matrix, $\mathbf{Y}$, whose rows are further randomly shuffled by an unknown permutation matrix $\mathbf{U}$. A new Bayes-optimal unlabeled compressed sensing (UCS) recovery algorithm is developed from the bilinear approximate message passing (Bi-VAMP) framework using non-separable and coupled priors on the rows and columns of the permutation matrix $\mathbf{U}$. In particular, standard unlabeled sensing is a special case of the proposed framework, and UCS further generalizes it by neither assuming a partially shuffled signal matrix $\mathbf{X}$ nor a small-sized permutation matrix $\mathbf{U}$. For the sake of theoretical performance prediction, we also conduct a state evolution (SE) analysis of the proposed algorithm and show its consistency with the asymptotic empirical mean-squared error (MSE). Numerical results demonstrate the effectiveness of the proposed UCS algorithm and its advantage over state-of-the-art baseline approaches in various applications. We also numerically examine the phase transition diagrams of UCS, thereby characterizing the detectability region as a function of the signal-to-noise ratio (SNR).

cs.IT↗

arXiv2024

Physically-Consistent Modeling and Optimization of Non-local RIS-Assisted Multi-User MIMO Communication Systems

Mutual Coupling (MC) emerges as an inherent feature in Reconfigurable Intelligent Surfaces (RISs), particularly, when they are fabricated with sub-wavelength inter-element spacing. Hence, any physically-consistent model of the RIS operation needs to accurately describe MC-induced effects. In addition, the design of the ElectroMagnetic (EM) transmit/receive radiation patterns constitutes another critical factor for efficient RIS operation. The latter two factors lead naturally to the emergence of non-local RIS structures, whose operation can be effectively described via non-diagonal phase shift matrices. In this paper, we focus on jointly optimizing MC and the radiation patterns in multi-user MIMO communication systems assisted by non-local RISs, which are modeled via the scattering parameters. We particularly present a novel problem formulation for the joint optimization of MC, radiation patterns, and the active and passive beamforming in a physically-consistent manner, considering either reflective or transmissive RIS setups. Differently from the current approaches that design the former two parameters on the fly, we present an offline optimization method which is solved for both considered RIS functionalities. Our extensive simulation results, using both parametric and geometric channel models, showcase the validity of the proposed optimization framework over benchmark schemes, indicating that improved performance is achievable without the need for optimizing MC and the radiation patterns of the RIS on the fly, which can be rather cumbersome.

cs.IT↗

arXiv2024

Lens-Type Redirective Intelligent Surfaces for Multi-User MIMO Communication

This paper explores the idea of using redirective reconfigurable intelligent surfaces (RedRIS) to overcome many of the challenges associated with the conventional reflective RIS. We develop a framework for jointly optimizing the switching matrix of the lens-type RedRIS ports along with the active precoding matrix at the base station (BS) and the receive scaling factor. A joint non-convex optimization problem is formulated under the minimum mean-square error (MMSE) criterion with the aim to maximize the spectral efficiency of each user. In the single-cell scenario, the optimum active precoding matrix at the multi-antenna BS and the receive scaling factor are found in closed-form by applying Lagrange optimization, while the optimal switching matrix of the lens-type RedRIS is obtained by means of a newly developed alternating optimization algorithm. We then extend the framework to the multi-cell scenario with single-antenna base stations that are aided by the same lens-type RedRIS. We further present two methods for reducing the number of effective connections of the RedRIS ports that result in appreciable overhead savings while enhancing the robustness of the system. The proposed RedRIS-based schemes are gauged against conventional reflective RIS-aided systems under both perfect and imperfect channel state information (CSI). The simulation results show the superiority of the proposed schemes in terms of overall throughput while incurring much less control overhead.

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arXiv2024

Next-slot OFDM-CSI Prediction: Multi-head Self-attention or State Space Model?

The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL architecture candidates for specific one-sided and two-sided use cases such as channel state estimation (CSI) feedback, CSI prediction, beam management, and positioning. In this paper, we set focus on the CSI prediction task and study the performance and generalization of the two main DL layers that are being extensively benchmarked within the DL community, namely, multi-head self-attention (MSA) and state-space model (SSM). We train and evaluate MSA and SSM layers to predict the next slot for uplink and downlink communication scenarios over urban microcell (UMi) and urban macrocell (UMa) OFDM 5G channel models. Our numerical results demonstrate that SSMs exhibit better prediction and generalization capabilities than MSAs only for SISO cases. For MIMO scenarios, however, the MSA layer outperforms the SSM one. While both layers represent potential DL architectures for future DL-enabled 5G use cases, the overall investigation of this paper favors MSAs over SSMs.

cs.IT↗

arXiv2024

Electromagnetically-Consistent Modeling and Optimization of Mutual Coupling in RIS-Assisted Multi-User MIMO Communication Systems

Mutual Coupling (MC) is an unavoidable feature in Reconfigurable Intelligent Surfaces (RISs) with sub-wavelength inter-element spacing. Its inherent presence naturally leads to non-local RIS structures, which can be efficiently described via non-diagonal phase shift matrices. In this paper, we focus on optimizing MC in RIS-assisted multi-user MIMO wireless communication systems. We particularly formulate a novel problem to jointly optimize active and passive beamforming as well as MC in a physically consistent manner. To characterize MC, we deploy scattering parameters and propose a novel approach to optimize them through an offline optimization method, rather than optimizing MC on the fly. Our numerical results showcase that the system performance increases with the proposed MC optimization, and this improvement is achievable without the need for optimizing MC on-the-fly, which can be rather cumbersome.

cs.IT↗

arXiv2024

Nonlocal Reconfigurable Intelligent Surfaces for Wireless Communication: Modeling and Physical Layer Aspects

Conventional Reconfigurable intelligent surfaces (RIS) for wireless communications have a local position-dependent (phase-gradient) scattering response on the surface. We consider more general RIS structures, called nonlocal (or redirective) RIS, that are capable of selectively manipulate the impinging waves depending on the incident angle. Redirective RIS have nonlocal wavefront-selective scattering behavior and can be implemented using multilayer arrays such as metalenses. We demonstrate that this more sophisticated type of surfaces has several advantages such as: lower overhead through coodebook-based reconfigurability, decoupled wave manipulations, and higher efficiency in multiuser scenarios via multifunctional operation. Additionally, redirective RIS architectures greatly benefit form the directional nature of wave propagation at high frequencies and can support integrated fronthaul and access (IFA) networks most efficiently. We also discuss the scalability and compactness issues and propose efficient nonlocal RIS architectures such as fractionated lens-based RIS and mirror-backed phase-masks structures that do not require additional control complexity and overhead while still offering better performance than conventional local RIS.

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