arXiv subjects

Hadi Sarieddeen

Publications and source records attributed to Hadi Sarieddeen.

At least 19 recordsLinked to original sources

arXiv2026

Learning-based near- versus far-field boundaries for ultra-massive MIMO communications

Signal processing techniques for wireless communications and sensing fundamentally differ between near-field and far-field propagation regimes. Accurately identifying the applicable propagation region is therefore essential for enabling efficient beamforming and channel estimation in ultra-massive MIMO (UM-MIMO) systems. This paper proposes a fully unsupervised learning framework to distinguish near-field from far-field propagation based solely on received signal measurements, before estimating the communication distance, and without relying on channel state information. The proposed approach exploits spatial signal power variations across subarrays of a UM array as a physics-inspired feature extraction stage, followed by the OPTICS clustering algorithm to infer the communication region. Simulation results under various system configurations and signal-to-noise ratio (SNR) levels demonstrate that the proposed method accurately identifies the near-field and far-field regions, showing agreement with theoretical boundaries.

cs.IT↗

arXiv2026

Exploiting Mutual Coupling Structure for Channel Estimation of Active RIS-Assisted Links

Accurate channel modeling and estimation of active reconfigurable intelligent surface (RIS)-assisted links with densely integrated elements are essential to fully unleashing this technology's potential. This work adopts a physically consistent model incorporating mutual coupling (MC) effects, modeled via scattering parameters, in RIS-aided communication. We formulate the MC-aware channel estimation as a compressed sensing (CS) problem. The MC effect leads to an increase in the sensing matrix dimensions. This increased dimensionality substantially elevates the complexity of the formulated CS problem. To overcome this, we propose a low-complexity estimator that leverages the structure of the scattering matrix and MC mechanisms to obtain a reduced-size design sensing matrix. Numerical results demonstrate that our approach outperforms MC-unaware estimators by several dBs, achieving accuracy comparable to fully MC-aware solutions but with significantly lower complexity.

cs.IT↗

arXiv2026

Channel-Adaptive Region Adjacency Graph Carriers for Semantic Image Communication

Semantic image communication seeks to preserve task-relevant scene structure under limited channel resources, but carriers are often dense latent tensors or grid-aligned semantic layouts that do not explicitly encode region-level relations. This work introduces a segmentation-derived region adjacency graph (RAG) carrier, termed channel-adaptive RAG (CA-RAG), for joint source-channel coding-style image communication. Nodes store interpretable region attributes, edges preserve adjacency, channel-adaptive graph simplification (CGS) controls the node budget, and semantic belief propagation refines noisy graph embeddings before diffusion-based reconstruction. On Cityscapes, pre-channel RAG payloads are several times smaller than compressed class-index layouts in a 2,000-image study. Under additive white Gaussian noise at signal-to-noise ratios from 0 to 15 dB, CA-RAG reports higher semantic consistency than deep joint source-channel coding and a same-decoder layout baseline, with comparable perceptual quality. At 10 dB, the full-budget rate-sweep point reaches mean intersection over union (mIoU) = 0.329 at approximately 3.3 x 10^3 channel uses, while the default adaptive-CGS setting reports mIoU = 0.294 at approximately 2.6 x 10^3 channel uses.

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arXiv2026

Where to Defend? Layer-Wise Adversarial Training for Robust Transformer-Based Semantic Communications

Deep learning-based semantic communication (DeepSC), a Transformer-based encoder-decoder, achieves semantic fidelity over noisy channels but remains vulnerable to adversarial perturbations injected at multiple stages of the pipeline. We present a layer-wise robustness framework that compares fast gradient sign method (FGSM), projected gradient descent (PGD), and l2-normalized fast gradient method (FGM) defenses at the embedding output, and then uses PGD to analyze three attack and defense points: the embedding output, encoder output, and channel-encoder bottleneck. We evaluate reconstruction on Europarl and UK Hansard and sentiment classification on SST2 and YELP under additive white Gaussian noise (AWGN) and Rayleigh fading. A first-order damage budget, {epsilon} times the l1 norm of the clean-input loss gradient at each injection point, predicts the attack-severity ordering, and the transfer matrix reveals asymmetric defense transfer: the embedding defense transfers strongly to encoder attacks, whereas encoder defenses degrade robustness against upstream attacks. For reconstruction, encoder-point training yields the largest matched gain but fails severely under embedding attacks; joint embedding-plus-encoder training retains comparable gains under encoder attacks while mitigating this mismatch failure, and Rayleigh fading attenuates robustness gains and degradation. For classification, three of four defenses collapse to constant predictors; only the channel-encoder defense remains non-degenerate, suggesting a protective role for the 128D-to-16D bottleneck. At a signal-to-noise ratio (SNR) of 9 dB and perturbation budget {epsilon} = 0.3, the matched encoder defense recovers bilingual evaluation understudy (BLEU) from ~ 0.10 to ~ 0.64 on Europarl/AWGN, whereas the mismatched encoder defense yields a -0.444 BLEU change relative to the undefended baseline under an embedding attack.

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arXiv2026

Privacy-Preserving Deep Joint Source-Channel Coding with In-Loop Concept Erasure

Deep joint source-channel coding (DeepJSCC) transmits learned semantic features efficiently but can leak sensitive attributes such as gender, race, or speaker identity. We propose LEAPSC (LEACE-in-the-loop privacy for semantic communication), whose core contribution is the integration of in-loop least-squares concept erasure (LEACE) within a variational information bottleneck (VIB) encoder. By periodically refitting the projection operator during training, LEAPSC couples the encoder dynamics to the erasure mechanism, driving attribute-conditional mean differences toward zero within each task-label group on the fitting sample. Additional components, namely conditional value-at-risk (CVaR) tail-sensitive privacy, feature-wise linear modulation (FiLM) signal-to-noise ratio conditioning, and Lagrangian dual ascent, improve robustness across channel conditions and over the high-leakage tail of samples. On CelebA, FairFace, and Google Speech Commands, LEAPSC reaches task accuracy of 0.862, 0.755, and 0.925 respectively, with attacker accuracy at or below the label-only floor on CelebA (0.548 vs. floor 0.580) and within 2 percentage points (pp) of chance elsewhere, improving over an information-bottleneck adversarial baseline (IBAL) at a matched 52-epoch budget by +3.6, +2.5, and +1.3 pp (Welch's t-test, p=0.019 on CelebA).

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arXiv2026

Krum-Inspired Central Teacher Selection and Residual Channel Bottlenecks for Efficient DeepSC

Deploying transformer-based semantic communication models on edge devices requires compression that preserves semantic fidelity under channel variability. We study a compressed deep semantic communication (DeepSC) student trained by multi-teacher knowledge distillation and combine two ideas: (i) a residual channel bottleneck that splits the transmitted representation into a base stream and a residual stream with unequal power allocation, and (ii) a Krum-inspired, medoid-style centrality criterion that selects a single central teacher from a five-model ensemble, applied at the logit and intermediate-feature levels and combined with a feature-dominant distillation loss. On the EuroParl benchmark, a two-layer student recovers about 98% of the four-layer teacher's bilingual evaluation understudy (BLEU)-1 under additive white Gaussian noise, with 93% of its BLEU-4 and 96% of its sentence-BERT (SBERT) score, and about 89%, 77%, and 87% of the teacher's BLEU-1, BLEU-4, and SBERT, respectively, under Rayleigh fading, while reducing non-embedding parameters by 1.33x and single-teacher inference latency by 1.79x (9.0x relative to a five-teacher ensemble used here as an upper-bound reference, not a deployment baseline). Controlled ablations over three seeds indicate complementary contributions of about +8.4% BLEU-1 (+9.6% SBERT) from the residual bottleneck and +3.0% (+2.9% SBERT) from centrality-based selection over mean aggregation. A decoder-mode ablation shows that the base stream alone recovers about two-thirds of the full BLEU-1 while the residual stream alone collapses, supporting the role of the residual as a refinement on top of the base.

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arXiv2026

FedGenSC: Federated Generative Semantic Communication with Channel-Aware Adaptation

Integrating generative adversarial networks (GANs) into federated semantic communication (SemCom) is a natural progression, as generative priors can recover semantic fidelity under channel distortion that discriminative decoders cannot. However, naive GAN federation introduces three failure modes that prior work has, to the best of our knowledge, neither identified nor resolved: discriminator aggregation instability under non-independent and identically distributed (non-IID) data, semantic drift caused by divergent local embedding spaces, and channel-agnostic generation that cannot adapt to heterogeneous link conditions. We propose federated generative semantic communication (FedGenSC), which mitigates all three by employing a global generator with local-only discriminators, providing cross-client semantic information through a semantic prototype bank, and conditioning generation on the instantaneous signal-to-noise ratio (SNR). Experiments on the Europarl dataset over Rayleigh fading channels (K=10 clients, Dirichlet α=0.5) show that FedGenSC under non-IID data outperforms the FedDeepSC baseline across the tested SNR range, achieving up to a 58.2% relative improvement in bilingual evaluation understudy (BLEU)-1 at 18 dB. Ablation studies confirm the independent contribution of each component.

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arXiv2026

Semantic-Aware Sub-Band Allocation for Terahertz Communications

This paper studies semantic-aware sub-band al- location for terahertz (THz) communication systems, where frequency-selective molecular absorption creates highly non- uniform sub-band qualities. Unlike conventional formulations, semantic fidelity depends nonlinearly on the signal-to-noise ratio (SNR) and is also sentence-specific, leading to a non-separable assignment problem that is generally not solvable using simple ordering-based policies. To address this, we use a sentence-BERT (SBERT)-based surrogate model that predicts semantic fidelity from the sentence embedding and sub-band SNR. We propose an importance-aware scheduler that assigns sentences to sub- bands based on their semantic contribution using an oracle utility function that captures importance-weighted semantic similarity across sentence-sub-band pairs. A neural scheduler is then trained through imitation learning to approximate the oracle policy at more than 200x lower runtime than full DeepSC- based oracle evaluation. Integrated with a deep-learning-enabled semantic communication (DeepSC) system, the proposed method consistently outperforms all benchmark schemes and approaches oracle-level performance under realistic THz channel conditions.

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arXiv2026

Physically Consistent Channel Modeling and Signal Processing for Reconfigurable Wireless Systems

Reconfigurable antennas are increasingly integrated into multi-antenna communication systems to exploit large apertures while reducing the hardware complexity, energy consumption, and implementation costs of classical massive arrays. Their reconfigurable electromagnetic (EM) properties, including dynamically varying radiation patterns and state-dependent mutual coupling, challenge the fixed-antenna and decoupled-port assumptions of conventional channel models. This motivates a physically consistent framework connecting Maxwell's equations, circuit theory, and information theory. In this tutorial, we develop a unified framework spanning three coupled dimensions: (i) reconfigurable antenna and transceiver architectures, (ii) physically consistent channel modeling, and (iii) physically consistent signal processing. We first establish a taxonomy covering tunable antennas, reconfigurable transceivers, and emerging array architectures, highlighting their reconfiguration mechanisms and hardware-performance trade-offs. We then develop modeling approaches based on Maxwell's equations, wavenumber-domain representations, multiport network theory, and computational electromagnetics, and use them to construct end-to-end channel and noise models that capture near-field propagation, mutual coupling, and circuit-level impairments. Building on these models, we examine architecture-aware channel estimation, beamforming, data detection, and channel decoding, emphasizing how physical structure reshapes algorithm design and performance-complexity trade-offs. Overall, the tutorial treats physical architecture, channel and noise models, and communication algorithms as coupled components of an end-to-end design, providing a unified foundation for physically consistent reconfigurable wireless systems.

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arXiv2026

SignDeepSC: A Semantic Signature-based Approach for Robust Semantic Communication

Semantic communication systems such as deep semantic communication (DeepSC) offer high efficiency but are vulnerable to adversarial attacks on their underlying neural networks. We address a physical-layer man-in-the-middle (MitM) threat in which an adversary injects perturbations into the transmitted signal to distort its meaning. We propose SignDeepSC, an architectural defense that achieves adversarial robustness without requiring explicit adversarial example generation during training. The approach is built on a perceiver-inspired semantic signature, a compact vector summary of the source features transmitted over a separate low-rate auxiliary channel. This signature is used by a self-repairing decoder that leverages cross-attention to correct distortions and can additionally drive a scrambler that shuffles the feature layout. We evaluate SignDeepSC over Rayleigh fading and additive white Gaussian noise channels under both single-step fast gradient sign method (FGSM) and iterative projected gradient descent (PGD) attacks. Under PGD ($ε= 0.7$), at 12~dB signal-to-noise ratio with Rayleigh fading, SignDeepSC achieves a bilingual evaluation understudy (BLEU-4) score of 0.237 and bidirectional encoder representations from transformers (BERT) sentence similarity of 0.646, outperforming all baselines without degrading clean-channel performance, when the signature channel is well protected.

cs.CR↗

arXiv2026

Hierarchical THz Near-Field Localization with Subarray Processing and Covariance Correction

Terahertz (THz)-band near-field (NF) localization offers high spatial resolution due to short wavelengths and distance-dependent wavefront curvature in NF multi-antenna systems. However, large arrays and dense deployments, necessary to mitigate THz path loss, raise the received signal dimensionality, creating computational overhead for localization. Furthermore, traditional two-dimensional (2D) subspace algorithms suffer from excessive complexity and poor robustness under coherent sources. This paper proposes a hierarchical localization framework based on subarray (SA) processing. The first step performs 1D estimation per SA to estimate local angles. The second step combines SA outputs to estimate distances, reducing the 2D search to two 1D searches. To handle the drawback of coherent sources, a transformer-based network predicts a covariance correction, refining subspace estimation. Simulations show that the proposed hierarchical algorithm lowers complexity by four orders of magnitude. The transformer-based covariance correction improves angular accuracy by 85 % and reduces range error by 6.5 m at 5 dB signal-to-noise ratio in a coherent scenario compared to multiple signal classification (MUSIC).

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arXiv2026

Analysis of UAV-Enabled IoT Networks with Energy Harvesting and Wake-Up Radio

This paper investigates an unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) architecture that integrates wake-up radio (WuR) and energy harvesting for sustainable device operation. In the proposed system, UAVs transmit radio-frequency (RF) signals that both trigger device activation and replenish stored energy. The IoT device's behavior is modeled as a discrete-time Markov chain, which captures the evolution of its battery level and operational state (asleep or awake), accounting for both energy harvesting and energy consumption. Leveraging stochastic geometry and discrete-time Markov-chain analysis, we develop a comprehensive mathematical framework to assess system performance. We reveal a tradeoff between transmission frequency and energy consumption, and demonstrate that tuning of system parameters, such as UAV density, can significantly improve both energy efficiency and transmission reliability.

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arXiv2026

Semantic Communications in the THz Band

Semantic and terahertz (THz)-band communications are algorithmic and spectral enablers of future wireless networks. This work investigates deep learning-based semantic communication (DeepSC) over THz channels. We show that DeepSC models trained solely under additive white Gaussian noise generalize well to the tested THz block- and fast-fading channels when receiver-side compensation is applied. To enable fully data-driven reception, we propose a lightweight neural detector that does not require channel state information (CSI). At 0.3 THz, DeepSC outperforms a throughput-matched traditional coded communication system baseline over 0-12 dB signal-to-noise ratio (SNR), achieving more than 50 percentage-point higher Bilingual Evaluation Understudy unigram (BLEU-1) score. The proposed pilot-free detector outperforms minimum mean square error (MMSE) equalization with both perfect and imperfect CSI and remains robust to frequency offsets up to 50 MHz, highlighting the resilience of semantic communication to THz channel impairments.

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arXiv2026

Symbol Error Analysis of Linear Receivers in Terahertz Channels under Channel-Noise Dependence

This paper develops a comprehensive framework for the performance analysis of linear detectors, namely zero-forcing (ZF) and minimum mean-square error (MMSE), under diverse terahertz (THz) channel conditions. Three fading models are considered: Rayleigh fading, the $α$--$μ$ distribution for indoor THz environments, and the mixture-gamma (MG) distribution for outdoor THz scenarios. Semi-analytical, approximate, and asymptotic expressions for the symbol error rate (SER) are derived, explicitly incorporating the correlation between the channel and the additive noise arising from hardware impairments. This correlation is characterized using both statistical approaches and copula-based methods to effectively capture complex dependency structures. The theoretical findings are validated through simulations, demonstrating strong agreement with the derived expressions and confirming the accuracy and robustness of the proposed framework. The results demonstrate the significant impact of channel--noise dependence on THz-band receiver performance and verify the expected performance degradation of biased MMSE receivers in point-to-point links employing higher-order quadrature amplitude modulation. Specifically, at a target SER of $10^{-3}$, a 70\% correlation results in approximately a 6.5~dB degradation in the effective signal-to-noise ratio, with mismatched MMSE detection incurring an additional 1~dB loss compared to ZF. Nonetheless, MMSE offers enhanced numerical stability under severe channel fading conditions, where channel inversion causes noise amplification.

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arXiv2026

On Near-Far-Field Boundaries in Wireless Systems

Near-field (NF) multi-antenna wireless communication and sensing have attracted growing research interest in recent years. A core question in this area is how to determine whether a wireless system is operating in the NF or far-field (FF) region. In this work, we propose a framework grounded in Maxwell's equations to analyze the transition region between the NF and FF, following the IEEE definition that specifies where the NF ends and the FF begins. Using this framework, we (i) compare a variety of traditional and recently introduced single-letter distance thresholds, often referred to as near-far-field boundaries, and (ii) conduct numerical experiments with both single- and multi-antenna wireless systems and with analytical models as well as full-wave electromagnetic simulations. Our results indicate that all of the considered single-letter distance thresholds are insufficient to predict the transition region between the NF and FF regions. Moreover, we highlight several important caveats associated with frequently (and recently) used NF and FF concepts.

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arXiv2026

Diversity Analysis for Terahertz Communication Systems under Small-Scale Fading

The terahertz (THz) band is a key enabler for future wireless systems, promising ultra-high data rates and dense spatial reuse. However, the reliability of THz links remains a major challenge due to severe path loss and small-scale fading effects, particularly in dynamic indoor and outdoor environments. This paper presents a comprehensive diversity analysis framework for THz communication systems under small-scale fading conditions. We model fading statistically using the generalized $α$-$μ$ distribution for indoor scenarios and the mixture of gamma (MG) model for outdoor propagation. We complement previous works that analyzed diversity under the $α$-$μ$ channels [1],[2]. In particular, we present new insights on diversity for the MG channel in addition to recovering the results of [2] using a different approach. Moreover, we derive asymptotic expressions for the bit error rate as a function of the inverse signal-to-noise ratio, recovering all of the $α$-$μ$ diversity results using a simpler approximation method. The analytical results are extensively validated through Monte Carlo simulations, demonstrating excellent agreement. Our findings show that diversity gains in THz systems are strongly influenced by the number of independent paths, the severity of fading, and frequency selectivity. The proposed framework provides system designers with clear guidelines for quantifying and optimizing diversity gains in emerging channel models, paving the way for more reliable high-frequency wireless links in next-generation networks.

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arXiv2025

Performance and Complexity Analysis of Terahertz-Band MIMO Detection

Achieving terabit-per-second (Tbps) data rates in terahertz (THz)-band communications requires bridging the complexity gap in baseband transceiver design. This work addresses the signal processing challenges associated with data detection in THz-band multiple-input multiple-output (MIMO) systems. We begin by analyzing the trade-offs between performance and complexity across various detection schemes and THz channel models, demonstrating significant complexity reduction by leveraging spatial parallelism across subspaces of correlated, typically ill-conditioned THz MIMO channels. We also derive accurate theoretical bounds on the detection error probability by incorporating THz-specific channel distributions and accounting for mismatches introduced by subspace decomposition. In addition, we propose a variation of subspace detectors that combines channel-matrix sorting, QR decomposition, and puncturing. Furthermore, under wideband THz UM-MIMO systems, we introduce a channel-matrix reuse strategy that minimizes exhaustive computations while maintaining reliable detection performance within a coherence bandwidth. Simulations over accurate THz channels show that the proposed efficient spatial parallelization schemes yield multi-dB performance gains, while the proposed reuse strategy offers significant computational savings with minimal performance degradation.

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arXiv2025

RIS-Aided Near-Field Channel Estimation under Mutual Coupling and Spatial Correlation

The integration of reconfigurable intelligent surfaces (RIS) with extremely large multiple-input multiple-output (MIMO) arrays at the base station has emerged as a key enabler for enhancing wireless network performance. However, this setup introduces high-dimensional channel matrices, leading to increased computational complexity and pilot overhead in channel estimation. Mutual coupling (MC) effects among densely packed unit cells, spatial correlation, and near-field propagation conditions further complicate the estimation process. Conventional estimators, such as linear minimum mean square error (MMSE), require channel statistics that are challenging to acquire for high-dimensional arrays, while least squares (LS) estimators suffer from performance limitations. To address these challenges, the reduced-subspace least squares (RS-LS) estimator leverages array geometry to enhance estimation accuracy. This work advances the promising RS-LS estimation algorithm by explicitly incorporating MC effects into the more realistic and challenging near-field propagation environment within the increasingly relevant generalized RIS-aided MIMO framework. Additionally, we investigate the impact of MC on the spatial degrees of freedom (DoF). Our analysis reveals that accounting for MC effects provides a significant performance gain of approximately 5 dB at an SNR of 5 dB, compared to conventional methods that ignore MC.

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