arXiv subjects

Mérouane Debbah

Publications and source records attributed to Mérouane Debbah.

At least 37 records · Page 2Linked to original sources

arXiv2026

Two-Layer Stacked Intelligent Metasurfaces: Balancing Performance and Complexity

Stacked intelligent metasurfaces (SIMs) have emerged as a powerful paradigm for wave-domain signal processing, enabling fine-grained control over electromagnetic (EM) propagation in next-generation wireless systems. However, conventional multi-layer SIMs often suffer from excessive structural complexity, high computational overhead, and significant power attenuation across layers, limiting their performance. In this paper, we first characterize SIMs from the perspectives of functionality, application, and layer configuration, revealing the inherent trade-offs between signal processing flexibility and power efficiency. Then, two representative 2-layer architectures, the meta-fiber-connected SIM (MF-SIM) and the flexible intelligent layered metasurface (FILM), are introduced, each advocating a distinct 2-layer SIM design philosophy. Moreover, we identify several open challenges in topology optimization for MF-SIM, shape control for FILM, and hybrid 2-layer architectures. Finally, case studies considering 2-layer MF-SIM and FILM assisted point-to-point multiple-input multiple-output (MIMO) and multi-user communication systems validate that properly designed 2-layer SIMs can significantly reduce power loss and optimization burden while maintaining good signal processing performance, offering a promising pathway toward practical SIM-enabled 6G systems.

cs.IT↗

arXiv2026

Artificial Noise Versus Artificial Noise Elimination: Redefining Scaling Laws of Physical Layer Security

Artificial noise (AN) is a key physical-layer security scheme for wireless communications over multiple-input multiple-output wiretap channels. Recently, artificial noise elimination (ANE) has emerged as a strategy to mitigate the impact of AN on eavesdroppers. However, the influence of ANE on the secrecy rate when counteracting AN has not been investigated. In this paper, we address this issue by establishing scaling laws for both average and instantaneous secrecy rates in the presence of AN and ANE. Based on the scaling laws, several derived corollaries provide insights into the mutual constraints between the number of transmit antennas, receive antennas, and antennas at eavesdroppers, revealing the interplay between these factors. A key corollary reveals that when the eavesdropper possesses more than twice as many antennas as the transmitter, secure communication may no longer be guaranteed. Additionally, by comparing scenarios where ANE counteracts AN with those where AN is not employed, this study identifies sufficient conditions under which AN remains effective. Finally, the derived secrecy rates provide guidelines for system design, even in the presence of advanced ANE countermeasures implemented by the eavesdropper.

cs.IT↗

arXiv2026

Large Language Model Empowered CSI Feedback in Massive MIMO Systems

Despite the success of large language models (LLMs) across domains, their potential for efficient channel state information (CSI) compression and feedback in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems remains largely unexplored yet increasingly important. In this paper, we propose a novel LLM-based framework for CSI feedback to exploit the potential of LLMs. We first reformulate the CSI compression feedback task as a masked token prediction task that aligns more closely with the functionality of LLMs. Subsequently, we design an information-theoretic mask selection strategy based on self-information, identifying and selecting CSI elements with the highest self-information at the user equipment (UE) for feedback. This ensures that masked tokens correspond to elements with lower self-information, while visible tokens correspond to elements with higher self-information, thus maximizing the accuracy of LLM predictions.

cs.IT↗

arXiv2026

RF-GPT: Teaching AI to See the Wireless World

Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, are still not natively supported by these models. Existing LLM-based approaches for telecom focus mainly on text and structured data, while conventional RF deep-learning models are built separately for specific signal-processing tasks, highlighting a clear gap between RF perception and high-level reasoning. To bridge this gap, we introduce RF-GPT, a radio-frequency language model (RFLM) that utilizes the visual encoders of multimodal LLMs to process and understand RF spectrograms. In this framework, complex in-phase/quadrature (IQ) waveforms are mapped to time-frequency spectrograms and then passed to pretrained visual encoders. The resulting representations are injected as RF tokens into a decoder-only LLM, which generates RF-grounded answers, explanations, and structured outputs. To train RF-GPT, we perform supervised instruction fine-tuning of a pretrained multimodal LLM using a fully synthetic RF corpus. Standards-compliant waveform generators produce wideband scenes for six wireless technologies, from which we derive time-frequency spectrograms, exact configuration metadata, and dense captions. A text-only LLM then converts these captions into RF-grounded instruction-answer pairs, yielding roughly 12,000 RF scenes and 0.625 million instruction examples without any manual labeling. Across benchmarks for wideband modulation classification, overlap analysis, wireless-technology recognition, WLAN user counting, and 5G NR information extraction, RF-GPT achieves strong multi-task performance, whereas general-purpose VLMs with no RF grounding largely fail.

eess.SP↗

arXiv2026

Seeing Radio: From Zero RF Priors to Explainable Modulation Recognition with Vision Language Models

Current RF machine-learning pipelines rely on task-specific deep networks for modulation classification and related tasks, but these models require custom architectures and labeled datasets for each problem, generalize poorly across channel conditions and SNRs, and offer little interpretability. In contrast, modern multimodal large language models (MLLMs) can integrate heterogeneous visual and textual data and exhibit strong cross-domain generalization and explanation capabilities. Our goal in this work is to explore whether vision-language models (VLMs) can be adapted to directly perceive RF signals and reason about modulation patterns without redesigning their architectures or injecting RF-specific inductive biases. To achieve this, we convert complex IQ streams into time-series, spectrogram, and joint RF visualizations, build a 57-class RF visual question answering benchmark, and show that lightweight parameter-efficient fine-tuning can enhance the accuracy of a general-purpose VLM from around 10% to nearly 90%, while ensuring robustness to noise and out-of-vocabulary modulations and the ability to produce human-readable rationales. The obtained results show that combining RF-to-image conversion with promptable VLMs provides a scalable and practical foundation for RF-aware AI systems in future 6G networks.

eess.SP↗

arXiv2026

Interference Propagation Analysis for Large-Scale Multi-RIS-Empowered Wireless Communications:An Epidemiological Perspective

Reconfigurable intelligent surfaces (RISs) have gained significant attention in recent years due to their ability to control the reflection of radio-frequency signals and reshape the wireless propagation environment. Unlike traditional studies that primarily focus on the advantages of RISs, this paper examines the negative impacts of RISs by investigating interference propagation caused by user mobility in downlink wireless systems. We employ a stochastic geometric model to simulate the locations of base stations and RISs using the Matérn hard core point process, while user locations are modeled with the homogeneous Poisson point process. We derive novel closed-form expressions for the power distributions of the received signal at the users and the interfering signal. Additionally, we present a novel expression for coverage probability and introduce the concept of interference propagation intensity. To characterize the dynamics of interference caused by user mobility, we adopt an epidemiological approach using the susceptible-infected-susceptible model. Finally, crucial factors influencing the propagation of interference are analyzed. Numerical results validate our theoretical analysis and provide suggestions for managing interference propagation in large-scale multi-RIS wireless communication networks.

cs.NI↗

arXiv2026

Trajectory-Aware Multi-RIS Activation and Configuration: A Riemannian Diffusion Method

Reconfigurable intelligent surfaces (RISs) offer a low-cost, energy-efficient means for enhancing wireless coverage. Yet, their inherently programmable reflections may unintentionally amplify interference, particularly in large-scale, multi-RIS-enabled mobile communication scenarios where dense user mobility and frequent line-of-sight overlaps can severely degrade the signal-to-interference-plus-noise ratio (SINR). To address this challenge, this paper presents a novel generative multi-RIS control framework that jointly optimizes the ON/OFF activation patterns of multiple RISs in the smart wireless environment and the phase configurations of the activated RISs based on predictions of multi-user trajectories and interference patterns. We specially design a long short-term memory (LSTM) artificial neural network, enriched with speed and heading features, to forecast multi-user trajectories, thereby enabling reconstruction of future channel state information. To overcome the highly nonconvex nature of the multi-RIS control problem, we develop a Riemannian diffusion model on the torus to generate geometry-consistent phase-configuration, where the reverse diffusion process is dynamically guided by reinforcement learning. We then rigorously derive the optimal ON/OFF states of the metasurfaces by comparing predicted achievable rates under RIS activation and deactivation conditions. Extensive simulations demonstrate that the proposed framework achieves up to 30\% SINR improvement over learning-based control and up to 44\% gain compared with the RIS always-on scheme, while consistently outperforming state-of-the-art baselines across different transmit powers, RIS configurations, and interference densities.

cs.NI↗

arXiv2026

Multi-target DoA estimation with a single Rydberg atomic receiver by spectral analysis of spatially-resolved fluorescence

Rydberg-based Direction-of-Arrival (DoA) estimation has been hampered by the complexity of receiver arrays and the single-target, narrow-band limitations of existing single-receiver methods. This paper introduces a novel approach that addresses these limitations. We demonstrate that by spatially resolving the fluorescence profile along the vapor cell, the multi-target problem can be effectively solved. Our approach hinges on the insight that by superimposing incoming signals with a strong local oscillator (LO), the complex atomic absorption pattern is linearized into a simple superposition of sinusoids. In this new representation, each spatial frequency uniquely and directly maps to the DoA of a target. This reduces the multi-target challenge into a spectral estimation problem, which we address using Prony's method. Our approach, termed Imaging-based Spectral Estimation (ISE), inherently supports multi-target detection and restores the full broadband capability of the sensor by removing the restrictive cell-length dependency. This development also shows potential for realizing multi-channel Rydberg receivers and the continuous-aperture sensing required for holographic multiple-input multiple-output (MIMO). We develop a comprehensive theoretical model, derive the Cramer-Rao Lower Bound (CRLB) as a performance benchmark, and present simulations validating the effectiveness of the approach to resolve multiple targets.

cs.IT↗

arXiv2026

Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance

The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting classical wireless communications and sensing. So far, most experimental studies have aimed for the proof of physical concepts to reveal its promise, while the practical signal model of RAQR-aided wireless communications and sensing remained under-explored. Furthermore, the performance of RAQR-based wireless receivers and their advantages over classical RF receivers have not been fully characterized. To fill these gaps, we introduce the RAQR to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow. Our scheme and model provide explicit design guidance to RAQR-aided wireless systems. We next study the performance of RAQR-aided wireless systems based on our model, and compare them to classical RF receivers. The results show that Doppler broadening-free RAQRs are capable of achieving a substantial received signal-to-noise ratio (SNR) gain of over $27$ decibel (dB) and $40$ dB in the photon shot limit and standard quantum limit regimes, respectively.

eess.SP↗

arXiv2026

Rydberg Atomic Quantum MIMO Receivers for The Multi-User Uplink

Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution for evolving wireless receivers from the classical to the quantum domain. To further unleash their great potential in wireless communications, we propose a flexible architecture for Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) receivers in the multi-user uplink. Then the corresponding signal model of the RAQ-MIMO system is constructed by paving the way from quantum physics to classical wireless communications. Explicitly, we outline the associated operating principles and transmission flow. We also validate the linearity of our model and its feasible region. Based on our model, we derive closed-form asymptotic formulas for the ergodic achievable rate (EAR) of both the maximum-ratio combining (MRC) and zero-forcing (ZF) receivers operating in uncorrelated fading channels (UFC) and the correlated fading channels (CFC), as well as in the standard quantum limit (SQL) and photon shot limit (PSL) regimes, respectively. Furthermore, we unveil that the EAR scales logarithmically without bound with the product of effective number $N_{\text{atom}}$ and coherence time $T_2$ of the atomic ensemble in the SQL regime, but exhibits non-monotonic trade-off between the collective atomic enhancement and optical-depth-dependent attenuation in the PSL regime. More particularly, the transmit power of users can be scaled down quadratically with $N_{\text{atom}} τ$, $τ\in \{ T_2, \frac{ {\cal C} (Ω_{\ell}) }{A_p} \}$, but the EAR per user retains fixed, by increasing $N_{\text{atom}}$ while retaining the sensor number $M \propto N_{\text{atom}} τ$ in the SQL regime or $M \propto \exp \big( \frac{N_{\text{atom}} {\bar χ}}{A_p} \big)$ in the PSL regime....

eess.SP↗

arXiv2026

Frontiers of Generative AI for Network Optimization: Theories, Limits, and Visions

While interest in the application of generative AI (GenAI) in network optimization has surged in recent years, its rapid progress has often overshadowed critical limitations intrinsic to generative models that remain insufficiently examined in existing literature. This survey provides a comprehensive review and critical analysis of GenAI in network optimization. We focus on the two dominant paradigms of GenAI including generative diffusion models (GDMs) and large pre-trained models (LPTMs), and organize our discussion around a categorization we introduce, dividing network optimization problems into two primary formulations: one-shot optimization and Markov decision process (MDP). We first trace key works, including foundational contributions from the AI community, and categorize current efforts in network optimization. We also review frontier applications of GDMs and LPTMs in other networking tasks, providing additional context. Furthermore, we present theoretical generalization bounds for GDMs in both one-shot and MDP settings, offering insights into the fundamental factors affecting model performance. Most importantly, we reflect on the overestimated perception of GenAI's general capabilities and caution against the all-in-one illusion it may convey. We highlight critical limitations, including difficulties in constraint satisfying, limited concept understanding, and the inherent probabilistic nature of outputs. We also propose key future directions, such as bridging the gap between generation and optimization. Although they are increasingly integrated in implementations, they differ fundamentally in both objectives and underlying mechanisms, necessitating a deeper understanding of their theoretical connections. Ultimately, this survey aims to provide a structured overview and a deeper insight into the strengths, limitations, and potential of GenAI in network optimization.

cs.NI↗

arXiv2026

Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications

This paper focuses on wireless multiple-input multiple-output (MIMO)-orthogonal frequency division multiplex (OFDM) receivers. Traditional wireless receivers have relied on mathematical modeling and Bayesian inference, achieving remarkable success in most areas but falling short in their ability to characterize channel matrices. Neural networks (NNs) have demonstrated significant potential in this aspect. Nevertheless, integrating traditional inference methods with NNs presents challenges, particularly in tracking the error progression. Given the inevitable presence of noise in wireless systems, generative models that are more resilient to noise are garnering increased attention. In this paper, we propose re-evaluating the MIMO-OFDM receiver using diffusion models, which is a common generative approach. With diffusion models, we can effectively leverage prior knowledge of channel matrices and incorporate traditional signal estimation components. Specifically, we explore the diffusion system and introduce an imagination-screening strategy to guide the diffusion process. Furthermore, diffusion models enable adaptation to varying noise levels and pilot schemes using the same NN, significantly reducing training and deployment costs. Simulated results reveal that, for pilot densities ranging from 4-6 pilots per 64-subcarrier block and signal-to-noise ratios (SNRs) from -4 dB to 0 dB, our proposed receiver reduces channel-reconstruction error by up to two times compared to leading deep-learning models, with the most pronounced improvements observed in low-pilot conditions. Additionally, performance enhancements can be achieved with a larger imagination size, despite increased computational complexity.

eess.SP↗

arXiv2025

Rydberg Atomic Quantum Receivers for Multi-Target DOA Estimation

Quantum sensing technologies have experienced rapid progresses since entering the `second quantum revolution'. Among various candidates, schemes relying on Rydberg atoms exhibit compelling advantages for detecting radio frequency signals. Based on this, Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution to classical wireless communication and sensing. To harness the advantages and exploit the potential of RAQRs in wireless sensing, we investigate the realization of the direction of arrival (DOA) estimation by RAQRs. Specifically, we first conceive a Rydberg atomic quantum uniform linear array (RAQ-ULA) aided wireless receiver for multi-target DOA detection and propose the corresponding signal model of this sensing system. Our model reveals that the presence of the radio-frequency local oscillator in the RAQ-ULA creates sensor gain mismatches, which degrade the DOA estimation significantly by employing the classical Estimation of Signal Parameters via Rotational Invariant Techniques (ESPRIT). To solve this sensor gain mismatch problem, we propose the Rydberg atomic quantum ESPRIT (RAQ-ESPRIT) relying on our model. Lastly, we characterize our scheme through numerical simulations, where the results exhibit that it is capable of reducing the estimation error of its classical counterpart on the order of $> 400$-fold and $> 9000$-fold in the PSL and SQL, respectively.

eess.SP↗

arXiv2025

Robust Precoding Designs of RSMA for Multiuser MIMO Systems

Rate-splitting multiple access (RSMA) has been studied for multiuser multiple-input multiple-output (MUMIMO) systems especially in the presence of imperfect channel state information (CSI) at the transmitter. However, its precoding designs that maximize the sum rate normally have high computational complexity. To implement an efficient RSMA scheme for the MU-MIMO system, in this work, we propose a novel robust precoding design, which can handle imperfect CSI. Specifically, we first adopt the generalized mutual information to construct a lower bound of the objective function in the sum rate maximization problem. Then, we apply a smooth lower bound of the non-smooth sum rate objective function to construct a new optimization problem. By revealing the relationship between the generalized signal-to-interference-plus-noise ratio and the minimum mean square error matrices, we transform the constructed problem into a tractable one. After decomposing the transformed problem into three subproblems, we investigate a new alternating precoding design based on sequential solutions. Simulation results demonstrate that the proposed precoding scheme achieves comparable performance to conventional methods, while significantly reducing the computational complexity.

cs.IT↗

arXiv2025

Graph-Aware Temporal Encoder Based Service Migration and Resource Allocation in Satellite Networks

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers.

cs.NI↗

arXiv2025

3D Gaussian Radiation Field Modeling for Integrated RIS-FAS Systems: Analysis and Optimization

The integration of reconfigurable intelligent surfaces (RIS) and fluid antenna systems (FAS) has attracted considerable attention due to its tremendous potential in enhancing wireless communication performance. However, under fast-fading channel conditions, rapidly and effectively performing joint optimization of the antenna positions in an FAS system and the RIS phase configuration remains a critical challenge. Traditional optimization methods typically rely on complex iterative computations, thus making it challenging to obtain optimal solutions in real time within dynamic channel environments. To address this issue, this paper introduces a field information-driven optimization method based on three-dimensional Gaussian radiation-field modeling for real-time optimization of integrated FAS-RIS systems. In the proposed approach, obstacles are treated as virtual transmitters and, by separately learning the amplitude and phase variations, the model can quickly generate high-precision channel information based on the transmitter's position. This design eliminates the need for extensive pilot overhead and cumbersome computations. On this framework, an alternating optimization scheme is presented to jointly optimize the FAS position and the RIS phase configuration. Simulation results demonstrate that the proposed method significantly outperforms existing approaches in terms of spectrum prediction accuracy, convergence speed, and minimum achievable rate, validating its effectiveness and practicality in fast-fading scenarios.

cs.NI↗

arXiv2025

Diffusion Models for Wireless Transceivers: From Pilot-Efficient Channel Estimation to AI-Native 6G Receivers

With the development of artificial intelligence (AI) techniques, implementing AI-based techniques to improve wireless transceivers becomes an emerging research topic. Within this context, AI-based channel characterization and estimation become the focus since these methods have not been solved by traditional methods very well and have become the bottleneck of transceiver efficiency in large-scale orthogonal frequency division multiplexing (OFDM) systems. Specifically, by formulating channel estimation as a generative AI problem, generative AI methods such as diffusion models (DMs) can efficiently deal with rough initial estimations and have great potential to cooperate with traditional signal processing methods. This paper focuses on the transceiver design of OFDM systems based on DMs, provides an illustration of the potential of DMs in wireless transceivers, and points out the related research directions brought by DMs. We also provide a proof-of-concept case study of further adapting DMs for better wireless receiver performance.

eess.SP↗

arXiv2025

Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs

This paper investigates underwater cooperative target detection using autonomous underwater vehicles (AUVs), with a focus on the critical trade-off between cooperation efficiency and communication covertness. To tackle this challenge, we first formulate a joint trajectory and power control optimization problem, and then present an innovative hierarchical action management framework to solve it. According to the hierarchical formulation, at the macro level, the master AUV models the agent selection process as a Markov decision process and deploys the proximal policy optimization algorithm for strategic task allocation. At the micro level, each selected agent's decentralized decision-making is modeled as a partially observable Markov decision process, and a multi-agent proximal policy optimization algorithm is used to dynamically adjust its trajectory and transmission power based on its local observations. Under the centralized training and decentralized execution paradigm, our target detection framework enables adaptive covert cooperation while satisfying both energy and mobility constraints. By comprehensively modeling the considered system, the involved signals and tasks, as well as energy consumption, theoretical insights and practical solutions for the efficient and secure operation of multiple AUVs are provided, offering significant implications for the execution of underwater covert communication tasks.

cs.LG↗