arXiv subjects

Georges Kaddoum

Publications and source records attributed to Georges Kaddoum.

At least 19 recordsLinked to original sources

arXiv2026

Entanglement Allocation for Quantum Integrated Sensing and Communication

Jointly supporting communication and distributed sensing over a shared, limited pool of entanglement resources is an attractive prospect for future quantum networks. We develop a network-level quantum integrated sensing and communication (Q-ISAC) framework in which a quantum base station dynamically allocates elementary Bell pairs between communication users and distributed sensing. Sensing resources are adaptively partitioned among multiple GHZ probes with dynamically selected sizes and sensing-node assignments. We derive an effective quantum Fisher information model accounting for probabilistic fusion, transmission loss, and memory decoherence, and formulate a finite-horizon allocation problem subject to communication QoS, expected sensing-information, sensing-reliability, and hardware constraints. A hierarchical constrained proximal policy optimization (HC-PPO) framework combines learned discrete sensing allocation with analytical continuous communication allocation. Numerical results show that GHZ probing requires approximately $27$--30% fewer sensing qubits than separable probing under modest probe-size caps and 65-67% fewer at the highest considered target under the same sensing requirements. Adaptive multi-GHZ partitioning provides greater robustness than single-GHZ sensing, while adaptive communication--sensing sharing outperforms the evaluated fixed splits. The proposed HC-PPO framework satisfies the joint requirements in at least 99.9% of test instances across the considered sensing targets and fusion probabilities. Monte Carlo evaluation separately shows that realized terminal information reached its target in 96-98% of realizations, distinguishing expected-information satisfaction from realized sensing performance.

quant-ph↗

arXiv2026

Multi-Timescale Context-Aware Radio Resource Optimization for Harmonious NTN-TN Coexistence: A Digital Twin and Open RAN Enabled Framework

Coexistence between non-terrestrial networks (NTNs) and terrestrial networks (TNs) is essential for future wireless systems, but terrestrial downlink transmissions can generate harmful aggregate interference at low-Earth orbit (LEO) satellite receivers. We present DT-STABLE, an Open Radio Access Network (O-RAN)-enabled, digital twin (DT)-assisted framework for context-aware NTN-TN coexistence. Using site-specific information, the DT estimates interference at LEO satellites, enabling proactive management with reduced real-time measurement overhead. We formulate joint beam activation and user association under RF-chain limits, load balancing, and aggregate satellite interference constraints; prove NP-hardness; and develop an efficient DT-guided scheduling algorithm. DT-STABLE couples short-timescale radio resource adaptation with periodic mid- and long-timescale DT updates through standardized O-RAN interfaces. Site-specific 28 GHz simulations under worst-case satellite geometry and low elevation angles show that DT-STABLE balances rate and interference utility, achieving 89.63% interference-to-noise ratio (INR) satisfaction and a mean sum rate of 210.96 bps/Hz, with nearly 100% INR satisfaction when satellite protection is prioritized. The framework reduces beam measurement overhead by 99.71% relative to exhaustive sweeping and achieves up to 33.1 times lower runtime than the Gurobi-based exact optimization benchmark.

cs.NI↗

arXiv2026

Net-Zero Power UAVs: Facilitating Quantum HRLLC in Multi-Hop UAV Repeaters

In this paper, we propose a novel joint optimization framework for autonomous, quantum-enabled UAV repeater networks designed to support hyper-reliable low-latency communications (HRLLC) in challenging environments such as post-explosion chemical plant operations. The proposed system uses a symmetric dual-UAV architecture with three sequential quantum channels operating under strict energy harvesting (EH) constraints. We formulate a highly challenging non-convex mixed-integer non-linear programming (MINLP) problem. This model jointly determines qubit allocation, 2-D uncrewed aerial vehicle (UAV) spatial positioning (distance and altitude), and transmit powers to minimize the total decoding error. Crucially, our framework explicitly models real-world quantum physics, including decoherence, gate imperfections, and entanglement degradation. Since the objective function is demonstrably non-convex, to efficiently solve the MINLP, we propose a quantum-resource-optimized GA (QRO-GA). The results of our numerical simulations show that the QRO-GA yields better results than benchmark methods and it is highly efficient, with the convergence speed significantly increasing with the total qubit allocation. Convergence is achieved in as few as 5 iterations for higher qubit allocation. Moreover, the results reveal a critical U-shaped tradeoff, proving that an optimal latency distribution within a strict $80\ μ$s transmission window is strictly required to maintain HRLLC reliability. In addition, we find that the QRO-GA successfully optimizes the 2-D UAV positioning to minimize total decoding error. Finally, it balances consumed and harvested power to achieve net-zero power UAVs system.

eess.SY↗

arXiv2026

QIEN: Quantum-Inspired Energy-Neutral UAV Trajectory for WPT-Enabled Industrial HRLLC

This paper introduces Quantum-Inspired Energy-Neutral (QIEN) UAV Trajectory for WPT-Enabled Industrial HRLLC, a novel framework for mobility-integrated trajectory and resource optimization in solar-powered uncrewed aerial vehicle (UAV) relay systems supporting autonomous mining robots in post-disaster environments. We formulate a mixed-integer nonlinear program (MINLP) that jointly optimizes UAV trajectory, finite blocklength allocation, transmit power, and wireless power transfer (WPT) over discrete time horizons to maximize aggregate data throughput. The system operates under strict extreme hyper-reliable low-latency communication (HRLLC) constraints, energy sustainability requirements via solar harvesting, and continuous robot power delivery. To solve this large-scale optimization problem, QIEN employs a hybrid Cooperative Quantum-behaved Particle Swarm Optimization (Co-QPSO) algorithm using quantum tunneling mechanisms and a heterogeneous swarm architecture. Our simulation results demonstrate that the proposed QIEN framework significantly outperforms benchmark algorithms, achieving successful convergence while maintaining energy-neutral operation and satisfying all reliability, power, and mobility constraints.

eess.SY↗

arXiv2026

New Approximations of Non-Separable MIMO Channels by Separable Channels for Accurate Ergodic Capacity Analysis

In recent years, owing to the high accuracy in characterizing non-separable channels prevalent in next-generation wireless applications, the classical Weichselberger channel model has gained widespread adoption in multiple-input multiple-output (MIMO) systems. However, its non-separable structure also introduces severe analytical complexity, leading to a lack of tractable mathematical frameworks in the literature and thus raises an urgent need for further research. To address the aforementioned analytical complexity, we first derive the nearest separable (double-correlated Rayleigh) fading model to the Weichselberger model under the Kullback-Leibler divergence (KLD), a problem equivalent to rank-1 nonnegative matrix factorization under the Itakura-Saito (IS) distance criterion. The results of our asymptotic analysis in the high-SNR regime reveal that the KLD-enabled approximation achieves a tighter capacity estimate than the conventional Kronecker model, especially in sparse and non-regular scattering environments. Yet, a key limitation of the KLD-enabled model is its tendency to mischaracterize the channel capacity in the low-SNR regime due to its inability to preserve total channel power. As a more robust alternative, we introduce a novel moment matching method (MMM) aimed at mapping the exact channel statistics to those of a Wishart distribution. Both the KLD-enabled and MMM-enabled separable channel directly enable the use of exact closed-form expressions for the ergodic capacity. Numerical results demonstrate that the MMM-enabled model consistently improves upon the capacity accuracy of the conventional Kronecker model across all SNR regimes.

cs.IT↗

arXiv2026

RIS-Aided ISAC in Cluttered Environments

In this paper, we analyze the performance of a communication-optimized reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system operating in a cluttered environment where multiple scatterers may interfere with the different types of reflected sensing signals. The RIS phases coherently combine the direct and reflected communication paths at the user equipment, whereas the corresponding radar returns remain generally misaligned. In addition, static scatterers near the radar act as environmental clutter that affects only the sensing function. For the communication link over small-scale fading, we derive an exact ergodic-capacity expression for the no-RIS baseline, a moment-matched Gamma approximation for the RIS-assisted link, and a Jensen upper bound, all of which are interpreted as upper bounds on the rate of the underlying binary phase-shift keying waveform. For sensing, our analysis focuses on the average signal-to-clutter-plus-noise ratio (SCNR) at the direct range-Doppler cell. Specifically, we derive the average powers of the direct, RIS-related, and scatterer returns, which scale as constant, linear, linear, quadratic, and constant, respectively, with the number of RIS elements. We then weigh them by the range and slow-time leakage responses to obtain the SCNR, thereby separating RIS-induced clutter from geometry-governed environmental clutter. Range and velocity estimation are evaluated using resolution-normalized metrics. Our Monte Carlo simulation results validate the analysis and show that zero-Doppler clutter leakage dominates the SCNR.

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arXiv2026

Distillation-Enhanced Continuous-Variable Quantum Teleportation for Satellite Communication Networks

Quantum teleportation (QT) over satellite-based free-space optical (FSO) channels is a promising approach for long-distance quantum communication. However, its performance is significantly degraded by atmospheric loss and turbulence. In this paper, we investigate continuous-variable (CV) QT in a dual-downlink scenario, where a satellite distributes entangled states to two ground stations. To mitigate channel-induced degradation, we employ a non-Gaussian entanglement distillation protocol based on the sequential application of photon addition and photon subtraction (PA-PS) on the weaker channel. The results show that the proposed scheme improves teleportation fidelity by up to 7.7% and enhances entanglement negativity by approximately 105% in the low-to-moderate (below 600 km) loss regime. In addition, we identify an optimal squeezing parameter that balances entanglement strength and noise sensitivity. Taken together, these results demonstrate the effectiveness of PA-PS distillation for improving CV quantum communication in realistic satellite networks. We further characterize the trade-off between fidelity gain and the heralded success probability of the protocol.

quant-ph↗

arXiv2026

End-to-End Deep Learning in Wireless Communication Systems: A Tutorial Review

The physical layer (PHY) in wireless communication systems has traditionally relied on model-based methods that are often optimized individually as independent blocks to perform tasks such as modulation, coding, and channel estimation. However, these approaches face challenges when it comes to capturing real-world nonlinearities, hardware imperfections, and increasing complexity in modern networks. This paper surveys advancements in applying deep learning (DL) for end-to-end PHY optimization by incorporating the autoencoder (AE) model as a powerful end-to-end DL framework to enable joint transmitter and receiver optimization and address challenges like dynamic channel conditions and scalability. We review cutting-edge DL models; their applications in PHY tasks such as modulation, error correction, and channel estimation; and their deployment in real-world scenarios, including point-to-point communication, multiple access, and interference channels. This work highlights the benefits of learning-based approaches over traditional methods, offering a comprehensive resource for researchers and engineers looking to innovate in next-generation wireless systems. Key insights and future directions are discussed to bridge the gap between theory and practical implementation.

cs.IT↗

arXiv2026

Secure Communications, Sensing, and Computing Towards Next-Generation Networks

Next-generation wireless networks are progressing beyond conventional connectivity to incorporate emerging sensing and computing capabilities. This convergence gives rise to integrated systems that enable not only uninterrupted communication, but also environmental awareness, intelligent decision-making, and novel applications that take advantage of these combined features. At the same time, this integration brings substantial security challenges. As computing, sensing, and communication become more tightly intertwined, the overall complexity of the system increases, creating new vulnerabilities and expanding the attack surface. The widespread deployment of data-heavy artificial intelligence applications further amplifies concerns regarding data security and privacy. This paper presents a comprehensive survey of security and privacy threats, along with potential countermeasures, in integrated wireless systems. We first review physical-layer security techniques for communication networks, and then investigate the security and privacy implications of semantic and pragmatic communications and their associated cross-layer design methodologies. For sensing functionalities, we pinpoint security and privacy risks at the levels of signal sources, propagation channels, and sensing targets, and summarize state-of-the-art defense strategies for each. The growing computational requirements of these applications drive the need for distributed computing over the network, which introduces additional risks such as data leakage, weak authentication, and multiple points of failure. We subsequently discuss secure coded computing approaches that can help overcome several of these challenges. Finally, we introduce unified security frameworks tailored to integrated communication-sensing-computing architectures, offering an end-to-end perspective on protecting future wireless systems.

cs.IT↗

arXiv2026

QoS-Aware Energy Optimization via Cell Switching in Heterogeneous Networks

The growing demand for mobile data services in dense urban areas has intensified the need for energy-efficient radio access networks (RANs) in future 6G systems. In this context, one promising strategy is cell switching (CS), which dynamically deactivates underutilized small base stations (SBSs) to reduce power consumption. However, while previous research explored CS primarily based on traffic load, ensuring user quality of service (QoS) under realistic channel conditions remains a challenge. In this paper, we propose a novel optimization-driven CS framework that jointly minimizes network power consumption and guarantees user QoS by enforcing a minimum received power threshold as part of offloading decisions. In contrast to prior load-based or learning-based approaches, our method explicitly integrates channel-aware information into the CS process, thus ensuring reliable service quality for offloaded users. Furthermore, flexibility of the proposed framework enables operators to adapt system behavior between energy-saving and QoS-preserving modes by tuning a single design parameter. Simulation results demonstrate that the proposed approach achieves up to 30% power savings as compared to baseline methods while fully maintaining QoS under diverse network conditions. Scalability and robustness of the proposed method in realistic heterogeneous networks (HetNets) further highlight its potential as a practical solution for sustainable 6G deployments.

eess.SY↗

arXiv2026

Sustainable Vertical Heterogeneous Networks: A Cell Switching Approach with High Altitude Platform Station

The rapid growth of radio access networks (RANs) is increasing energy consumption and challenging the sustainability of future systems. We consider a dense-urban vertical heterogeneous network (vHetNet) comprising a high-altitude platform station (HAPS) acting as a super macro base station, a terrestrial macro base station (MBS), and multiple small base stations (SBSs). We propose a HAPS-enhanced cell-switching algorithm that selectively deactivates SBSs based on their traffic load and the capacity and channel conditions of both the MBS and HAPS. The resulting energy-minimization problem, subject to an outage-based quality-of-service (QoS) constraint, is formulated as a mixed-integer nonlinear program and reformulated into a mixed-integer program for efficient solution. Using realistic 3GPP channel models, simulations show substantial energy savings versus All-ON, terrestrial cell switching, and sorting benchmarks. Relative to All-ON, the proposed method reduces power consumption by up to 77% at low loads and about 40% at high loads; a NoQoS variant achieves up to 90% and 47%, respectively. The approach maintains high served-traffic levels and provides a tunable trade-off between power efficiency and outage-based QoS, supporting scalable and sustainable 6G deployments.

eess.SY↗

arXiv2025

Distributed Beamforming in Massive MIMO Communication for a Constellation of Airborne Platform Stations

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.

eess.SY↗

arXiv2025

Beamforming for Massive MIMO Aerial Communications: A Robust and Scalable DRL Approach

This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate maximization under imperfect local channel state information (CSI). We propose a novel entropy-based multi-agent deep reinforcement learning (DRL) approach where each non-terrestrial base station (NTBS) independently computes its beamforming vector using a Fourier Neural Operator (FNO) to capture long-range dependencies in the frequency domain. To ensure scalability and robustness, the proposed framework integrates transfer learning based on a conjugate prior mechanism and a low-rank decomposition (LRD) technique, thus enabling efficient support for large-scale user deployments and aerial layers. Our simulation results demonstrate the superiority of the proposed method over baseline schemes including WMMSE, ZF, MRT, CNN-based DRL, and the deep deterministic policy gradient (DDPG) method in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability across varying network sizes and user densities. Furthermore, we show that the proposed method achieves significant computational efficiency compared to CNN-based and WMMSE methods, while reducing communication overhead in comparison with shared-critic DRL approaches.

eess.SP↗

arXiv2025

Hybrid Quantum-Classical Detection for RIS-Assisted SC-FDE via Grover Adaptive Search

Wideband and low-latency requirements in sixth-generation (6G) networks demand detectors that approach maximum-likelihood (ML) performance without incurring exponential complexity. This work develops a hybrid quantum-classical detection framework for reconfigurable intelligent surface (RIS)-assisted single-carrier (SC) frequency-domain equalization (FDE) over frequency-selective channels. The ML detection objective is reformulated as a quadratic unconstrained binary optimization (QUBO) problem and solved via Grover adaptive search (GAS). To accelerate convergence, we introduce a frequency-domain MMSE threshold that exploits the circulant structure of SC-FDE channels, yielding low-complexity initialization. The framework is evaluated across varying channel lengths and RIS sizes, confirming robustness and scalability. In addition, GAS requirements are quantified through register widths and gate counts, and its query complexity is analyzed to characterize the algorithm's cost for block transmission in frequency-selective channels. Quantum circuit simulations are conducted in Qiskit under both ideal and noisy conditions. In the ideal case, the detector achieves near-optimal performance while benefiting from Grover's quadratic speedup, reducing the search cost from from O(M^N) exhaustive evaluations to O(SQRT(M^N)) oracle queries. Under noise, the shallow depth of the GAS circuits, aided by MMSE initialization, makes depolarizing errors negligible, while readout errors introduce moderate degradation yet still preserve performance close to the MMSE baseline. These results establish the feasibility of quantum-enhanced detection for RIS-assisted broadband communications, highlighting both algorithmic scalability and practical robustness for 6G networks.

cs.NI↗

arXiv2025

Dual-Domain Deep Learning-Assisted NOMA-CSK Systems for Secure and Efficient Vehicular Communications

Ensuring secure and efficient multi-user (MU) transmission is critical for vehicular communication systems. Chaos-based modulation schemes have garnered considerable interest due to their benefits in physical layer security. However, most existing MU chaotic communication systems, particularly those based on non-coherent detection, suffer from low spectral efficiency due to reference signal transmission, and limited user connectivity under orthogonal multiple access (OMA). While non-orthogonal schemes, such as sparse code multiple access (SCMA)-based DCSK, have been explored, they face high computational complexity and inflexible scalability due to their fixed codebook designs. This paper proposes a deep learning-assisted power domain non-orthogonal multiple access chaos shift keying (DL-NOMA-CSK) system for vehicular communications. A deep neural network (DNN)-based demodulator is designed to learn intrinsic chaotic signal characteristics during offline training, thereby eliminating the need for chaotic synchronization or reference signal transmission. The demodulator employs a dual-domain feature extraction architecture that jointly processes the time-domain and frequency-domain information of chaotic signals, enhancing feature learning under dynamic channels. The DNN is integrated into the successive interference cancellation (SIC) framework to mitigate error propagation issues. Theoretical analysis and extensive simulations demonstrate that the proposed system achieves superior performance in terms of spectral efficiency (SE), energy efficiency (EE), bit error rate (BER), security, and robustness, while maintaining lower computational complexity compared to traditional MU-DCSK and existing DL-aided schemes. These advantages validate its practical viability for secure vehicular communications.

cs.IT↗

arXiv2025

Enhancing Resilience Against Jamming Attacks: A Cooperative Anti-Jamming Method Using Direction Estimation

The inherent vulnerability of wireless communication necessitates strategies to enhance its security, particularly in the face of jamming attacks. This paper uses the collaborations of multiple sensing nodes (SNs) in the wireless network to present a cooperative anti-jamming approach (CAJ) designed to neutralize the impact of jamming attacks. We propose an eigenvector (EV) method to estimate the direction of the channel vector from pilot symbols. Through our analysis, we demonstrate that with an adequate number of pilot symbols, the performance of the proposed EV method is comparable to the scenario where the perfect channel state information (CSI) is utilized. Both analytical formulas and simulations illustrate the excellent performance of the proposed EV-CAJ under strong jamming signals. Considering severe jamming, the proposed EV-CAJ method exhibits only a 0.7 dB degradation compared to the case without jamming especially when the number of SNs is significantly larger than the number of jamming nodes (JNs). Moreover, the extension of the proposed method can handle multiple jammers at the expense of degrees of freedom (DoF). We also investigate the method's ability to remain robust in fast-fading channels with different coherence times. Our proposed approach demonstrates good resilience, particularly when the ratio of the channel's coherence time to the time frame is small. This is especially important in the case of mobile jammers with large Doppler shifts.

cs.IT↗

arXiv2025

Cooperative Jamming Detection Using Low-Rank Structure of Received Signal Matrix

Wireless communication can be simply subjected to malicious attacks due to its open nature and shared medium. Detecting jamming attacks is the first and necessary step to adopt the anti-jamming strategies. This paper presents novel cooperative jamming detection methods that use the low-rank structure of the received signal matrix. We employed the likelihood ratio test to propose detectors for various scenarios. We regarded several scenarios with different numbers of friendly and jamming nodes and different levels of available statistical information on noise. We also provided an analytical examination of the false alarm performance of one of the proposed detectors, which can be used to adjust the detection threshold. We discussed the synthetic signal generation and the Monte Carlo (MC)-based threshold setting method, where knowledge of the distribution of the jamming-free signal, as well as several parameters such as noise variance and channel state information (CSI), is required to accurately generate synthetic signals for threshold estimation. Extensive simulations reveal that the proposed detectors outperform several existing methods, offering robust and accurate jamming detection in a collaborative network of sensing nodes.

cs.IT↗

arXiv2025

Design of A New Multiple-Chirp-Rate Index Modulation for LoRa Networks

We propose a multiple chirp rate index modulation (MCR-IM) system based on Zadoff-Chu (ZC) sequences that overcomes the problems of low transmission rate and large-scale access in classical LoRa networks. We demonstrate the extremely low cross-correlation of MCR-IM signals across different spread factors, showing that the proposed MCR-IM system also inherits the characteristics of ZC sequences modulation. Moreover, we derive an approximate closed-form expression for the bit-error rate (BER) of the proposed MCR-IM system over Nakagami-m fading channels. Simulation results confirm the accuracy of the derived closed-form expression and demonstrate that the MCR-IM system achieves higher levels of spectral efficiency (SE) compared to existing systems. In this context, assigning multiple chirp rates to each user results in a reduction in the number of parallel channels. To mitigate this issue, we propose a peak detection based successive interference cancellation (PD-SIC) algorithm to accommodate more users. Compared to orthogonal scatter chirp spreading spectrum system that names OrthoRa, the MCR-IM system with PD-SIC algorithm achieves lower BER levels. For a similar number of collision signals, the throughput of the MCR-IM system is enhanced by 16% to 21%. Owing to these advantages, the proposed MCR-IM is well suited for large-scale, high-rate LoRa network applications.

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