arXiv subjects

Faouzi Bellili

Publications and source records attributed to Faouzi Bellili.

At least 19 recordsLinked to original sources

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.

cs.IT↗

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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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

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

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

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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arXiv2024

Vector Approximate Message Passing With Arbitrary I.I.D. Noise Priors

Approximate message passing (AMP) algorithms are devised under the Gaussianity assumption of the measurement noise vector. In this work, we relax this assumption within the vector AMP (VAMP) framework to arbitrary independent and identically distributed (i.i.d.) noise priors. We do so by rederiving the linear minimum mean square error (LMMSE) to accommodate both the noise and signal estimations within the message passing steps of VAMP. Numerical results demonstrate how our proposed algorithm handles non-Gaussian noise models as compared to VAMP. This extension to general noise priors enables the use of AMP algorithms in a wider range of engineering applications where non-Gaussian noise models are more appropriate.

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arXiv2023

Age-Limited Capacity of Massive MIMO

We investigate the age-limited capacity of the Gaussian many channel with total $N$ users, out of which a random subset of $K_{a}$ users are active in any transmission period, and a large-scale antenna array at the base station (BS). In an uplink scenario where the transmission power is fixed among the users, we consider the setting in which both the number of users, $N$, and the number of antennas at the BS, $M$, are allowed to grow large at a fixed ratio $ζ= {M}/{N}$. Assuming perfect channel state information (CSI) at the receiver, we derive the achievability bound under maximal ratio combining. As the number of active users, $K_{a}$, increases, the achievable spectral efficiency is found to increase monotonically to a limit $\log_2\left(1+\frac{M}{K_{a}}\right)$. Further extensions of the analysis to the zero-forcing receiver as well as imperfect CSI are provided, demonstrating the channel estimation penalty in terms of the mean squared error in estimation. Using the age of information (AoI) metric, first coined by Kaul et al., as our measure of data timeliness or freshness, we investigate the trade-offs between the AoI and spectral efficiency in the context massive connectivity with large-scale receiving antenna arrays. As an extension of Liu and Yu, based on our large system analysis, we provide an accurate characterization of the asymptotic (finite system size) spectral efficiency as a function of the number of antennas and the number of users, the attempt probability, and the AoI. It is found that while the spectral efficiency can be made large, the penalty is an increase in the minimum AoI obtainable. The proposed achievability bound is further compared against recent massive MIMO-based massive unsourced random access (URA) schemes.

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arXiv2023

From Multilayer Perceptron to GPT: A Reflection on Deep Learning Research for Wireless Physical Layer

Most research studies on deep learning (DL) applied to the physical layer of wireless communication do not put forward the critical role of the accuracy-generalization trade-off in developing and evaluating practical algorithms. To highlight the disadvantage of this common practice, we revisit a data decoding example from one of the first papers introducing DL-based end-to-end wireless communication systems to the research community and promoting the use of artificial intelligence (AI)/DL for the wireless physical layer. We then put forward two key trade-offs in designing DL models for communication, namely, accuracy versus generalization and compression versus latency. We discuss their relevance in the context of wireless communications use cases using emerging DL models including large language models (LLMs). Finally, we summarize our proposed evaluation guidelines to enhance the research impact of DL on wireless communications. These guidelines are an attempt to reconcile the empirical nature of DL research with the rigorous requirement metrics of wireless communications systems.

cs.IT↗

arXiv2023

Super-Wideband Massive MIMO

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin "bandwidth gain". Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted "disconnected" designs that disregard MC by putting faith in the half-wavelength spacing rule.

cs.IT↗

arXiv2023

Channel Estimation for Multicarrier Systems with Tightly-Coupled Broadband Arrays

This paper develops a linear minimum mean-square error (LMMSE) channel estimator for single and multicarrier systems that takes advantage of the mutual coupling in antenna arrays. We model the mutual coupling through multiport networks and express the single-user multiple-input multiple-output (MIMO) communication channel in terms of the impedance and scattering parameters of the antenna arrays. We put forward a novel scattering description of the communication channel which requires only the scattering parameters of the arrays as well as the terminated far-field embedded antenna patterns. In multi-antenna single-carrier systems under frequency-flat channels, we show that neglecting the mutual coupling effects leads to inaccurate characterization of the channel and noise correlations. We also extend the analysis to frequency-selective multicarrier channels wherein we further demonstrate that the coupling between the antenna elements within each array increases the number of resolvable channel taps. Standard LMMSE estimators based on existing inaccurate channel models become sub-optimal when applied to the new physically consistent model. We hence develop a new LMMSE estimator that calibrates the coupling and optimally estimates the MIMO channel. It is shown that appropriately accounting for mutual coupling through the developed physically consistent model leads to remarkable performance improvements both in terms of channel estimation accuracy and achievable rate. We demonstrate those gains in a rich-scattering environment using a connected array of slot antennas both at the transmitter and receiver sides.

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arXiv2023

Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

cs.LG↗

arXiv2023

Physically Consistent Models for Intelligent Reflective Surface-assisted Communications under Mutual Coupling and Element Size Constraint

We investigate the benefits of mutual coupling effects between the passive elements of intelligent reconfigurable surfaces (IRSs) on maximizing the achievable rate of downlink Internet-of-Things (IoT) networks. In this paper, we present an electromagnetic (EM) coupling model for IRSs whose elements are connected minimum scattering antennas (i.e., dipoles). Using Chu's theory, we incorporate the finite antenna size constraint on each element of the IRS to obtain the IRS mutual impedance matrix. By maximizing the IRS phase shiters using the gradient ascent procedure, our numerical results show that mutual coupling is indeed crucial to avoid the achievable rate degradation when the spacing between IRS elements is down to a fraction of the wavelength.

cs.IT↗

arXiv2022

Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.

cs.IT↗

arXiv2021

Achievable Rate of Near-Field Communications Based on Physically Consistent Models

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications. By incorporating the finite antenna size constraint using Chu's theory and under the assumption of canonical-minimum scattering, we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we establish the achievable rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

cs.IT↗

arXiv2021

Optimizing Binary Symptom Checkers via Approximate Message Passing

Symptom checkers have been widely adopted as an intelligent e-healthcare application during the ongoing pandemic crisis. Their performance have been limited by the fine-grained quality of the collected medical knowledge between symptom and diseases. While the binarization of the relationships between symptoms and diseases simplifies the data collection process, it also leads to non-convex optimization problems during the inference step. In this paper, we formulate the symptom checking problem as an underdertermined non-convex optimization problem, thereby justifying the use of the compressive sensing framework to solve it. We show that the generalized vector approximate message passing (G-VAMP) algorithm provides the best performance for binary symptom checkers.

cs.LG↗

arXiv2021

Modulating Intelligent Surfaces for Multi-User MIMO Systems: Beamforming and Modulation Design

This paper introduces a novel approach of utilizing the reconfigurable intelligent surface (RIS) for joint data modulation and signal beamforming in a multi-user downlink cellular network by leveraging the idea of backscatter communication. We present a general framework in which the RIS, referred to as modulating intelligent surface (MIS) in this paper, is used to: i) beamform the signals for a set of users whose data modulation is already performed by the base station (BS), and at the same time, ii) embed the data of a different set of users by passively modulating the deliberately sent carrier signals from the BS to the RIS. To maximize each user's spectral efficiency, a joint non-convex optimization problem is formulated under the sum minimum mean-square error (MMSE) criterion. Alternating optimization is used to divide the original joint problem into two tasks of: i) separately optimizing the MIS phase-shifts for passive beamforming along with data embedding for the BS- and MIS-served users, respectively, and ii) jointly optimizing the active precoder and the receive scaling factor for the BS- and MIS-served users, respectively. While the solution to the latter joint problem is found in closed-form using traditional optimization techniques, the optimal phase-shifts at the MIS are obtained by deriving the appropriate optimization-oriented vector approximate message passing (OOVAMP) algorithm. Moreover, the original joint problem is solved under both ideal and practical constraints on the MIS phase shifts, namely, the unimodular constraint and assuming each MIS element to be terminated by a variable reactive load. The proposed MIS-assisted scheme is compared against state-of-the-art RIS-assisted wireless communication schemes and simulation results reveal that it brings substantial improvements in terms of system throughput while supporting a much higher number of users.

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