arXiv subjects

Bruno Clerckx

Publications and source records attributed to Bruno Clerckx.

At least 55 records · Page 3Linked to original sources

arXiv2026

Optimality Analysis of RSMA Degenerating to SDMA Under Imperfect SIC

This document serves as supplementary material for our journal submission, providing detailed mathematical proofs and derivations that support the results presented in the main manuscript. Specifically, under a modeling framework that jointly considers transceiver hardware impairments and imperfect successive interference cancellation (SIC), we systematically derive and prove from an optimality perspective that: when the residual interference coefficient approaches 1 (i.e., SIC becomes severely ineffective), there exists an optimal solution such that the common stream beamformer satisfies $\bm w_c^\star=\bm 0$, and hence the optimal rate-splitting multiple access (RSMA) transmission structure degenerates into space division multiple access (SDMA). This conclusion provides a verifiable theoretical justification for the convergence phenomenon observed in simulations, namely that "the RSMA performance gradually approaches that of SDMA as SIC degrades", and can also serve as a reference for multiple-access selection and system design in SIC-limited scenarios.

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arXiv2026

Movable Signals with Dual-Polarized Fixed Intelligent Surfaces: Beyond Diagonal Reflection Matrices

This paper investigates wireless systems aided by dual-polarized intelligent surfaces. We compare reconfigurable intelligent surface (RIS), which adjust their reflection matrices, with movable signals operating with fixed intelligent surface (FIS), which adjust the signal frequency while the surface properties remain fixed. For both RIS and FIS, we consider surfaces with a diagonal reflection matrix, named diagonal RIS/FIS, and surfaces with a reflection matrix not limited to being diagonal, named beyond-diagonal RIS/FIS. Movable signals with FIS always outperform RIS, achieving at least a fourfold gain. When transmitter and receiver polarizations differ, beyond-diagonal FIS further enhances performance.

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arXiv2026

An AMP-Based Asymptotic Analysis For Nonlinear One-Bit Precoding

This paper focuses on the asymptotic analysis of a class of nonlinear one-bit precoding schemes under Rayleigh fading channels. The considered scheme employs a convex-relaxation-then-quantization (CRQ) approach to the well-known minimum mean square error (MMSE) model, which includes the classical one-bit precoder SQUID as a special case. To analyze its asymptotic behavior, we develop a novel analytical framework based on approximate message passing (AMP). We show that, the statistical properties of the considered scheme can be asymptotically characterized by a scalar ``signal plus Gaussian noise'' model. Based on this, we further derive a closed-form expression for the symbol error probability (SEP) in the large-system limit, which quantitatively characterizes the impact of both system and model parameters on SEP performance. Simulation results validate our analysis and also demonstrate that performance gains over SQUID can be achieved by appropriately tuning the parameters involved in the considered model.

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arXiv2026

Microwave Linear Analog Computer (MiLAC)-aided Multiuser MISO: Fundamental Limits and Beamforming Design

As wireless communication systems evolve toward the 6G era, ultra-massive/gigantic MIMO is envisioned as a key enabling technology. Recently, microwave linear analog computer (MiLAC) has emerged as a promising approach to realize beamforming entirely in the analog domain, thereby alleviating the scalability challenges associated with gigantic MIMO. In this paper, we investigate the fundamental beamforming flexibility and design of lossless and reciprocal MiLAC-aided beamforming for MU-MISO systems. We first provide a rigorous characterization of the set of beamforming matrices achievable by MiLAC. Based on this characterization, we prove that MiLAC-aided beamforming does not generally achieve the full flexibility of digital beamforming, while offering greater flexibility than conventional phase-shifter-based analog beamforming. Furthermore, we propose a hybrid digital-MiLAC architecture and show that it achieves digital beamforming flexibility when the number of radio frequency (RF) chains equals the number of data streams, halving that required by conventional hybrid beamforming. We then formulate the MiLAC-aided sum-rate maximization problem for MU-MISO systems. To solve the problem efficiently, we reformulate the MiLAC-related constraints as a convex linear matrix inequality and establish a low-dimensional subspace property that significantly reduces the problem dimension. Leveraging these results, we propose WMMSE-based algorithms for solving the resulting problem. Simulation results demonstrate that MiLAC-aided beamforming achieves performance close to that of digital beamforming in gigantic MIMO systems. Compared with hybrid beamforming, it achieves comparable or superior performance with lower hardware and computational complexity by avoiding symbol-level digital processing and enabling low-resolution digital-to-analog converters (DACs).

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arXiv2026

Movable Beyond-Diagonal Reconfigurable Intelligent Surfaces: Moving, Interconnecting, or Both?

This letter proposes a movable beyond-diagonal reconfigurable intelligent surfaces (MA-BD-RIS) design, combining inter-element connectivity and movability for channel enhancement. We study a MA-BD-RIS assisted multi-user multiple input single output system where beamforming, BD-RIS configuration, and elements positions are jointly optimized to maximize the sum-rate. An efficient algorithm is developed, incorporating closed-form beamforming, a low-complexity partially proximal alternating direction method of multipliers for BD-RIS design, and successive convex approximation for element placement. Simulations show that the high-movability structure yields superior performance in small-scale RIS and rich scattering scenarios, while the high-connectivity structure dominates in large-scale RIS and massive transmit array configurations.

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arXiv2026

Beamforming and Waveform Optimization for RF Wireless Power Transfer with Beyond Diagonal Reconfigurable Intelligent Surfaces

Radio frequency (RF) wireless power transfer (WPT) is a promising technology to seamlessly charge low-power devices, but its low end-to-end power transfer efficiency remains a critical challenge. To address the latter, low-cost transmit/radiating architectures, e.g., based on reconfigurable intelligent surfaces (RISs), have shown great potential. Beyond diagonal (BD) RIS is a novel branch of RIS offering enhanced performance over traditional diagonal RIS (D-RIS) in wireless communications, but its potential gains in RF-WPT remain unexplored. Motivated by this, we analyze a BD-RIS-assisted single-antenna RF-WPT system to charge a single rectifier, and formulate a joint beamforming and multi-carrier waveform optimization problem aiming to maximize the harvested power. We propose two solutions relying on semi-definite programming for fully connected BD-RIS, a successive convex approximation (SCA)-based beamforming approach, and an efficient low-complexity iterative method relying on SCA. Numerical results show that the proposed algorithms converge and that adding transmit sub-carriers or RIS elements improves the harvesting performance. We show that the transmit power budget impacts the relative power allocation among different sub-carriers depending on the rectifier's operating regime, while BD-RIS shapes the cascade channel differently for frequency-selective and flat scenarios. Finally, we verify by simulation that BD-RIS and D-RIS achieve the same performance under pure far-field line-of-sight conditions (in the absence of mutual coupling). Meanwhile, BD-RIS outperforms D-RIS as the non-line-of-sight components of the channel become dominant.

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arXiv2025

A Uniform Pilot and Data Payload Optimization Framework for OTFS-Based ISAC

The orthogonal time frequency space (OTFS) signal is considered a promising solution for high-mobility wireless environments. It manages Doppler effects by utilizing delay-Doppler (DD) domain processing. However, the relatively long OTFS frame duration could introduce considerable sensing or communication latency when radar and communication are performed separately. By operating in a dual-functional radar and communication (DFRC) mode, the OTFS system performs sensing and data transmission simultaneously, thereby reducing the resulting latency. Nevertheless, the optimal OTFS DFRC signal strategy remains insufficiently explored. This paper investigates the optimal signal design for OTFS DFRC systems, focusing on pilot symbol design and data symbol power allocation. Specifically, we derive a channel capacity lower bound metric for communication that considers channel estimation errors in OTFS. For sensing, we derive an integrated sidelobe level (ISL), accounting for the randomness of the data symbols alongside the deterministic pilot symbols. Leveraging the above metrics, we formulate an optimization problem that balances radar and communication performance, and then solve it using an alternating optimization framework. We validate the proposed signal through numerical analysis and Monte Carlo simulations. Our analysis shows that OTFS DFRC enforces a deterministic pilot signal that is characterized by a concentrated peak in the DD domain, which furnishes a common structure in the DD domain facilitating sensing and channel estimation, with data multiplexed in other DD grids, thereby unifying sensing and communication within a single OTFS signal. Compared with conventional OTFS signals, the proposed OTFS DFRC signal expands the achievable sensing-communication performance region, delivering at least a 9.45 dB ISL suppression for sensing and a 4.82 dB SINR ratio gain for communication.

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arXiv2025

A Tutorial on Beyond-Diagonal Reconfigurable Intelligent Surfaces: Modeling, Architectures, System Design and Optimization, and Applications

Written by its inventors, this first tutorial on Beyond-Diagonal Reconfigurable Intelligent Surfaces (BD-RISs) provides the readers with the basics and fundamental tools necessary to appreciate, understand, and contribute to this emerging and disruptive technology. Conventional (Diagonal) RISs (D-RISs) are characterized by a diagonal scattering matrix $\mathbfΘ$ such that the wave manipulation flexibility of D-RIS is extremely limited. In contrast, BD-RIS refers to a novel and general framework for RIS where its scattering matrix is not limited to be diagonal (hence, the ``beyond-diagonal'' terminology) and consequently, all entries of $\mathbfΘ$ can potentially help shaping waves for much higher manipulation flexibility. This physically means that BD-RIS can artificially engineer and reconfigure coupling across elements of the surface thanks to inter-element reconfigurable components which allow waves absorbed by one element to flow through other elements. Consequently, BD-RIS opens the door to more general and versatile intelligent surfaces that subsumes existing RIS architectures as special cases. In this tutorial, we share all the secret sauce to model, design, and optimize BD-RIS and make BD-RIS transformative in many different applications. Topics discussed include physics-consistent and multi-port network-aided modeling; transmitting, reflecting, hybrid, and multi-sector mode analysis; reciprocal and non-reciprocal architecture designs and optimal performance-complexity Pareto frontier of BD-RIS; signal processing, optimization, and channel estimation for BD-RIS; hardware impairments (discrete-value impedance and admittance, lossy interconnections and components, wideband effects, mutual coupling) of BD-RIS; benefits and applications of BD-RIS in communications, sensing, power transfer.

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arXiv2025

A Secure Full-Duplex Wireless Circulator enabled by Non-Reciprocal Beyond-Diagonal RIS

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has arisen as a promising technology for enhancing wireless communication systems by enabling flexible and intelligent wave manipulation. This is achieved through the interconnections among the ports of the impedance network, enabling wave reconfiguration when they flow through the surface. Thus, the output wave at one port depends on waves impinging on neighboring ports, allowing non-local control of both phase and magnitude. Non-reciprocal (NR)-BD-RIS further enhances this capability by breaking circuit reciprocity and, consequently, channel reciprocity. In contrast to conventional reciprocal (R)-BD-RIS and diagonal (D)-RIS that are constrained by circuit and channel reciprocity such that they only allow bidirectional communications, i.e., between UE1 and UE2, NR-BD-RIS can additionally enable uni-directional communications, that is, UE1 to UE2 to UE3, hence effectively enabling a wireless circulator. Specifically, this paper introduces a novel application of NR-BD-RIS in full-duplex (FD) wireless circulators, where multiple FD devices communicate via an NR-BD-RIS. This system is particularly beneficial for secure transmission, as it enforces one-way communication among FD devices, suppresses signal from all other users (UE), and thus prevents eavesdropping. In addition, a physics-compliant system model is considered by incorporating structural scattering, also known as specular reflection. By accounting for this effect, the advantages of NR-BD-RIS are further validated. Specifically, we formulate an sum-rate maximization problem and propose an iterative optimization algorithm that employs block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Numerical evaluations illustrate that NR-BD-RIS outperforms conventional R-BD-RIS and D-RIS in terms of sum-rate and secrecy rate.

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arXiv2025

Rate-Splitting Multiple Access for Integrated Sensing and Communications: A First Experimental Study

A canonical use case of Integrated Sensing and Communications (ISAC) in multiple-input multiple-output (MIMO) systems involves a multi-antenna transmitter communicating with $K$ users and sensing targets in its vicinity. For this setup, precoder and multiple access designs are of utmost importance, as the limited transmit power budget must be efficiently directed towards the desired directions (users and targets) to maximize both communications and sensing performance. This problem has been widely investigated analytically under various design choices, in particular (a) whether or not a dedicated sensing signal is needed, and (b) for different MIMO multiple access techniques, such as Space Division Multiple Access (SDMA) and Rate-Splitting Multiple Access (RSMA). However, a conclusive answer on which design choice achieves the best ISAC performance, backed by experimental results, remains elusive. We address this vacuum by experimentally evaluating and comparing RSMA and SDMA for communicating with two users $(K = 2)$ and sensing (ranging) one target. Over three scenarios that are representative of \emph{vehicular} ISAC, covering different levels of inter-user interference and separation/integration between sensing and communications, we show that RSMA without a dedicated sensing signal achieves better ISAC performance -- i.e., higher sum throughput (up to $50\%$ peak throughput gain) for similar radar SNR (between $20$ to $24{\rm dB}$) -- than SDMA with a dedicated sensing signal. This first-ever experimental study of RSMA ISAC demonstrates the feasibility and the superiority of RSMA for future multi-functional wireless systems.

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arXiv2025

Analog Computing for Signal Processing and Communications -- Part I: Computing with Microwave Networks

Analog computing has been recently revived due to its potential for energy-efficient and highly parallel computations. In this two-part paper, we explore analog computers that linearly process microwave signals, named microwave linear analog computers (MiLACs), and their applications in signal processing and communications. In Part I of this paper, we model a MiLAC as a multiport microwave network with tunable impedance components, enabling the execution of mathematical operations by reconfiguring the microwave network and applying input signals at its ports. We demonstrate that a MiLAC can efficiently compute the linear minimum mean square error (LMMSE) estimator and matrix inversion, with remarkably low computational complexity. Specifically, a matrix can be inverted with complexity growing with the square of its size. We also show how a MiLAC can be used jointly with digital operations to implement sophisticated algorithms such as the Kalman filter. To enhance practicability, we propose a design of MiLAC based on lossless impedance components, reducing power consumption and eliminating the need for costly active components. In Part II of this paper, we investigate the applications of MiLACs in wireless communications, highlighting their potential to enable future wireless systems by executing computations and beamforming in the analog domain.

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arXiv2025

Analog Computing for Signal Processing and Communications -- Part II: Toward Gigantic MIMO Beamforming

Analog-domain operations offer a promising solution to accelerating signal processing and enabling future multiple-input multiple-output (MIMO) communications with thousands of antennas. In Part I of this paper, we have introduced a microwave linear analog computer (MiLAC) as an analog computer that processes microwave signals linearly, demonstrating its potential to reduce the computational complexity of specific signal processing tasks. In Part II of this paper, we extend these benefits to wireless communications, showcasing how MiLAC enables gigantic MIMO beamforming entirely in the analog domain. MiLAC-aided beamforming enables the maximum flexibility and performance of digital beamforming, while significantly reducing hardware costs by minimizing the number of radio-frequency (RF) chains and only relying on low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In addition, it eliminates per-symbol operations by completely avoiding digital-domain processing and remarkably reduces the computational complexity of zero-forcing (ZF), which scales quadratically with the number of antennas instead of cubically. It also processes signals with fixed matrices, e.g., the discrete Fourier transform (DFT), directly in the analog domain. Numerical results show that it can perform ZF and DFT with a computational complexity reduction of up to $1.5\times 10^4$ and $4.0\times 10^7$ times, respectively, compared to digital beamforming.

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arXiv2025

FAS-RSMA: Can Fluid Antennas Elevate RSMA Performance?

As 6G networks demand massive connectivity and stronger interference control, rate-splitting multiple access (RSMA) is attractive because it superposes a common stream and user-private streams and remains effective under imperfect CSIT and heterogeneous traffic. In practical multiuser deployments, two considerations arise: the common stream decoding constraint imposed by the weakest user, and residual inter-user interference can remain non-negligible, particularly in single-input single-output (SISO) broadcast settings and under an imperfect CSIT scenario. Motivated by prior advances of RSMA research, we investigate a complementary mechanism-fluid antenna systems (FAS), with dynamic port reconfiguration supplies adaptive spatial selectivity without altering the RSMA signaling structure. Can FAS help alleviate these considerations and enhance RSMA performance? We develop a tractable correlation-aware analytical framework based on block-correlation models, including constant block correlation (CBC) and variable block correlation (VBC), to capture realistic spatial dependence among ports. Closed-form expressions are derived for outage probability (OP) and average capacity (AC), revealing how port reconfiguration strengthens the weakest effective channel and improves SINR through higher channel gains and lower relative noise impact. Monte Carlo simulations verify the analysis and show that VBC matches simulations more tightly than CBC across all port configurations. Finally, FAS-RSMA provides clear gains over conventional antenna systems and NOMA, achieving lower OP and higher AC by combining RSMA interference management with FAS spatial diversity.

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arXiv2025

Optimal and Suboptimal Decoders under Finite-Alphabet Interference: A Mismatched Decoding Perspective

Interference widely exists in communication systems and is often not optimally treated at the receivers due to limited knowledge and/or computational burden. Evolutions of receivers have been proposed to balance complexity and spectral efficiency, for example, for 6G, while commonly used performance metrics, such as capacity and mutual information (MI), fail to capture the suboptimal treatment of interference, leading to potentially inaccurate performance evaluations. Mismatched decoding is an information-theoretic tool for analyzing communications with suboptimal decoders. In this work, we use mismatched decoding to analyze communications with decoders that treat interference suboptimally, aiming at more accurate performance metrics. Specifically, we consider a finite-alphabet input Gaussian channel under interference, representative of modern systems, where the decoder can be matched (optimal) or mismatched (suboptimal) to the channel. The matched capacity is derived using MI, while a lower bound on the mismatched capacity under various decoding metrics is derived using generalized mutual information (GMI). We show that the decoding metric in the proposed channel model is closely related to the behavior of the demodulator in bit-interleaved coded modulation (BICM) systems. Simulations illustrate that GMI/MI accurately predicts the throughput of BICM-type systems {with various demodulators}. Finally, we extend the channel model and the GMI to multiple antenna cases, with an example of multi-user multiple-input-single-output (MU-MISO) precoder optimization problem considering GMI under different decoding strategies. In short, this work discovers new insights about the impact of interference, proposes novel receivers, and introduces a new design and performance evaluation framework that more accurately captures the effect of interference.

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arXiv2025

Lossy Beyond Diagonal Reconfigurable Intelligent Surfaces: Modeling and Optimization

Beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as an advancement and generalization of the conventional diagonal RIS (D-RIS) by introducing tunable interconnections between RIS elements, enabling smarter wave manipulation and enlarged coverage. While BD-RIS has demonstrated advantages over D-RIS in various aspects, most existing works rely on the assumption of a lossless model, leaving practical considerations unaddressed. This paper thus proposes a lossy BD-RIS model and develops corresponding optimization algorithms for various BD-RIS-aided communication systems. First, by leveraging admittance parameter analysis, we model each tunable admittance based on a lumped circuit with losses and derive an expression of a circle characterizing the real and imaginary parts of each tunable admittance. We then consider the received signal power maximization in single-user single-input single-output (SISO) systems with the proposed lossy BD-RIS model. To solve the optimization problem, we design an effective algorithm by carefully exploiting the problem structure. Specifically, an alternating direction method of multipliers (ADMM) framework is custom-designed to deal with the complicated constraints associated with lossy BD-RIS. Furthermore, we extend the proposed algorithmic framework to more general multiuser multiple-input single-output (MU-MISO) systems, where the transmit precoder and BD-RIS scattering matrix are jointly designed to maximize the sum-rate of the system. Finally, simulation results demonstrate that all BD-RIS architectures still outperform D-RIS in the presence of losses, but the optimal BD-RIS architectures in the lossless case are not necessarily optimal in the lossy case, e.g. group-connected BD-RIS can outperform fully- and tree-connected BD-RISs in SISO systems with relatively high losses, whereas the opposite always holds true in the lossless case.

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arXiv2025

Global Optimal Closed-Form Solutions for Intelligent Surfaces With Mutual Coupling: Is Mutual Coupling Detrimental or Beneficial?

Reconfigurable Intelligent Surface (RIS) is a breakthrough technology enabling the dynamic control of the propagation environment in wireless communications through programmable surfaces. To improve the flexibility of conventional diagonal RIS (D-RIS), beyond diagonal RIS (BD-RIS) has emerged as a family of more general RIS architectures. However, D-RIS and BD-RIS have been commonly explored neglecting mutual coupling effects, while the global optimization of RIS with mutual coupling, its performance limits, and scaling laws remain unexplored. This study addresses these gaps by deriving global optimal closed-form solutions for BD-RIS with mutual coupling to maximize the channel gain, specifically fully- and tree-connected RISs. Besides, we provide the expression of the maximum channel gain achievable in the presence of mutual coupling and its scaling law in closed form. By using the derived scaling laws, we analytically prove that mutual coupling increases the channel gain on average under Rayleigh fading channels. Our theoretical analysis, confirmed by numerical simulations, shows that both fully- and tree-connected RISs with mutual coupling achieve the same channel gain upper bound when optimized with the proposed global optimal solutions. Furthermore, we observe that a mutual coupling-unaware optimization of RIS can cause a channel gain degradation of up to 5 dB.

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arXiv2025

Physics-Compliant Modeling and Scaling Laws of Multi-RIS Aided MIMO Systems

Reconfigurable intelligent surface (RIS) enables the control of wireless channels to improve coverage. To further extend coverage, multi-RIS aided systems have been explored, where multiple RISs steer the signal via a multi-hop path. However, deriving a physics-compliant channel model for multi-RIS aided systems is still an open problem. In this study, we fill this gap by modeling multi-RIS aided systems through multiport network theory, and deriving a channel model accounting for impedance mismatch, mutual coupling, and structural scattering. The derived physics-compliant model differs from the model widely used in literature, which omits the RIS structural scattering. To quantify this difference, we derive the channel gain scaling laws of the two models under line-of-sight (LoS) and multipath channels. Theoretical insights, validated by numerical results, show an important discrepancy between the physics-compliant and the widely used models, increasing with the number of RISs and multipath richness. In a multi-hop system aided by four 128-element RISs with multipath channels, optimizing the RISs using the widely used model and applying their solutions to the physics-compliant model achieves only 7% of the maximum channel gain. This highlights how severely mismatched channel models can be, calling for more accurate models in communication theory.

cs.IT↗

arXiv2025

Signal Design for OTFS Dual-Functional Radar and Communications with Imperfect CSI

Orthogonal time frequency space (OTFS) offers significant advantages in managing mobility for both wireless sensing and communication systems, making it a promising candidate for dual-functional radar-communication (DFRC). However, the optimal signal design that fully exploits OTFS's potential in DFRC has not been sufficiently explored. This paper addresses this gap by formulating an optimization problem for signal design in DFRC-OTFS, incorporating both pilot-symbol design for channel estimation and data-power allocation. Specifically, we employ the integrated sidelobe level (ISL) of the ambiguity function as a radar metric, accounting for the randomness of the data symbols alongside the deterministic pilot symbols. For communication, we derive a channel capacity lower bound metric that considers channel estimation errors in OTFS. We maximize the weighted sum of sensing and communication metrics and solve the optimization problem via an alternating optimization framework. Simulations indicate that the proposed signal significantly improves the sensing-communication performance region compared with conventional signal schemes, achieving at least a 9.44 dB gain in ISL suppression for sensing, and a 4.82 dB gain in the signal-to-interference-plus-noise ratio (SINR) for communication.

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