arXiv subjects

Yahia Medjahdi

Publications and source records attributed to Yahia Medjahdi.

13 recordsLinked to original sources

arXiv2026

Impact of PA Nonlinearities on AFDM Sensing: A Matched Filtering Perspective

Affine Frequency Division Multiplexing (AFDM) is a promising waveform for integrated sensing and communications (ISAC) due to its Doppler resilience. However, such waveforms exhibit a high peak-to-average power ratio (PAPR), and they require highly linear Power Amplifiers (PAs), which conflicts with ISAC requirements, where high transmit power for sensing is always beneficial. This paper investigates the impact of PA nonlinearities on the sensing performance of AFDM. Using the Bussgang decomposition of the nonlinearly amplified AFDM signal, we analyze the delay-Doppler ambiguity function for the sensing matched filtering and show that AFDM's sidelobes remain nearly unaffected. Simulation results confirm that AFDM outperforms OFDM in target detection under high Doppler and nonlinear amplification, highlighting its suitability for the sensing task.

eess.SP↗

arXiv2026

X-RACE: XAI-assisted Recurrent neural network Attribution for Channel Estimation

Deep learning models, notably Long Short-Term Memory (LSTM), have demonstrated promising performance in channel estimation for high-mobility vehicular environments. However, their black-box nature and architectural overhead limit trustworthiness and efficiency. Classical explainable AI (XAI) methods rely on costly iterative processes, offering only input-level filtering without addressing architectural fine-tuning. To overcome these limitations, this paper proposes the XAI-assisted Recurrent neural network Attribution for Channel Estimation (X-RACE) framework. X-RACE uses a low-complexity, one-shot dual-optimization strategy to simultaneously evaluate and prune irrelevant input subcarriers and internal hidden units. Furthermore, we propose novel temporal XAI metrics: Saturation Time, Importance Drift, and Relevance Contrast to characterize the LSTM's learning dynamics and memory convergence. Extensive simulations demonstrate that X-RACE reduces inference complexity by at least 44.1% while improving or preserving Bit Error Rate (BER) performance, outperforming classical XAI schemes.

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arXiv2026

Distortion-Aware Integrated Sensing and Communication with Affine Filter Bank Modulation

The stringent energy-efficiency requirements of future Integrated Sensing and Communications (ISAC) systems are fundamentally challenged. Unlike conventional communication systems, ISAC transmitters must radiate significantly higher power to ensure reliable target detection, forcing the High-Power Amplifier (HPA) to operate closer to saturation, where nonlinear distortions become unavoidable. Consequently, the robustness of every candidate ISAC waveform to HPA nonlinearities must be carefully assessed. In this context, this paper investigates the robustness of Affine Filter Bank Modulation (AFBM), a recently proposed waveform that combines the delay-Doppler resilience of affine modulation with reduced Peak-to-Average Power Ratio (PAPR) and improved spectral containment. We develop a statistical characterization of the Ambiguity Function (AF) of the amplified AFBM waveform, deriving approximate expressions for its mean, variance, and Rician-distributed magnitude. Furthermore, a low-complexity Gaussian belief propagation receiver accounting for HPA nonlinearities is proposed for communication detection. Simulation results validate the analytical framework and demonstrate that AFBM preserves favorable sensing characteristics and robust Bit Error Rate (BER) performance even under severe nonlinear amplification.

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arXiv2026

Low-Complexity Sensing-Aware PAPR Reduction for AFDM-based ISAC Systems

Integrated sensing and communication (ISAC) has emerged as a key technology for future wireless networks by enabling communication and environmental sensing through a common waveform and hardware platform. Among the candidate waveforms for ISAC, Affine Frequency Division Multiplexing (AFDM) had attracted significant attention due to its robustness in high-mobility environments, but it suffers from a high peak-to-average power ratio (PAPR). In this paper, we propose a sensing-aware chirp-subcarrier reservation (CSR) framework that reduces PAPR while improving ranging performance. The proposed method combines low-complexity gradient-based PAPR minimization with a randomized local search that exploits the phase sensitivity of the AFDM autocorrelation function to suppress delay low-ambiguity-zone (LAZ) sidelobes. Numerical results show that the proposed scheme achieves significant PAPR reduction together with significant sidelobe suppression, resulting in improved weak-target detection performance.

eess.SP↗

arXiv2026

Phase Uniformity Detector for GRSMReceivers in mmWave and Sub-THz Bands

This paper introduces a phase-domain statistical detector, the Phase Uniformity Detector (PUD), for binary hypothesis testing in Generalized Receive Spatial Modulation (GRSM) systems. The PUD uses direct RF sampling to obtain received signal samples, their phases are modeled via Directional Statistics (DS). A Generalized Likelihood Ratio Test (GLRT) is derived and reduced to a Rayleigh uniformity test with a closed-form, noise-variance-independent threshold. Unlike conventional Energy Detection (ED), the PUD offers robust spatial detection under Independent Local Oscillator Phase Noise (ILO-PN), remaining insensitive to energy fluctuations and noise uncertainty. Additionally, a phase-coherence-aware combining scheme mitigates ILO-PN without requiring estimation.

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arXiv2026

X-REFINE: XAI-based RElevance input-Filtering and archItecture fiNe-tuning for channel Estimation

AI-native architectures are vital for 6G wireless communications. The black-box nature and high complexity of deep learning models employed in critical applications, such as channel estimation, limit their practical deployment. While perturbation-based eXplainable Artificial Intelligence (XAI) solutions offer input filtering, they often neglect internal structural optimization. We propose X-REFINE, an XAI-based framework for joint input-filtering and architecture fine-tuning. By utilizing a decomposition-based, sign-stabilized LRP epsilon rule, X-REFINE backpropagates predictions to derive high-resolution relevance scores for both subcarriers and hidden neurons. This enables a reliable optimization that identifies the most reliable model components. Simulation results demonstrate that X-REFINE achieves a superior performance-complexity-interpretability trade-off compared to the external perturbation-based XAI frameworks, significantly reducing computational complexity while maintaining robust bit error rate (BER) performance.

cs.LG↗

arXiv2026

On the Robustness of AFBM Sensing to Power Amplifier Nonlinearities

We investigate the impact of power amplifier (PA) nonlinearities on the sensing performance of affine filter bank modulation (AFBM). While AFBM offers several advantageous properties for integrated sensing and communications (ISAC) - including reduced out-of-band emission (OOBE), low peak-to-average power ratio (PAPR), and natural robustness to doubly-dispersive (DD) channel effects - mitigating waveform distortion typically requires highly linear PAs. This creates a fundamental contradiction with ISAC applications, which demand high transmit power for reliable sensing. Our analytical results reveal that the structure of the effective AFBM modulation matrix dictates how distortion propagates within the ambiguity function (AF). Furthermore, simulations demonstrate that both the AF and the overall sensing performance of AFBM remain remarkably insensitive to such nonlinearities. These findings highlight the robustness of AFBM, making it a highly viable candidate for practical ISAC deployments constrained by hardware impairments.

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arXiv2026

Gaussian Phase Noise Effects on Hybrid Precoding MIMO Systems for Sub-THz Transmission

The sub-THz spectrum offers numerous advantages, including massive multiple-input multiple-output (MIMO) technology with large antenna arrays that enhance spectral efficiency (SE) of future systems. Hybrid precoding (HP) thus emerges as a cost-effective alternative to fully digital precoding regarding complexity and energy consumption. However, sub-THz frequencies introduce hardware challenges, particularly phase noise (PN) from local oscillators (LOs). We analyze PN impact on MIMO systems using HP, leveraging singular value decomposition and common LO architecture. We adopt the Gaussian PN (GPN) model, recognized as accurate for describing PN behavior in sub-THz transmissions. We derive a lower bound on achievable SE and provide closed-form bit error rate expressions for quadrature amplitude modulation (QAM), specifically 4-QAM and 16-QAM, under high-SNR and strong GPN conditions. These analytical results are validated through Monte Carlo simulations. We show that GPN can be effectively counteracted with a single pilot symbol in single-user MIMO systems, unlike single-input single-output systems where mitigation proves infeasible. Simulation results compare conventional QAM against polar-QAM tailored for GPN-impaired systems. Finally, we introduce perspectives for further improvements in performance and energy efficiency.

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arXiv2026

Exploiting Spatial Modulation for Strong PhaseNoise Mitigation in mmWave Massive MIMO

This letter investigates phase noise (PN) mitigation in generalized receiver spatial modulation (GRSM) massive MIMO systems at mmWave under a common local oscillator (CLO). Under CLO, the received energy remains invariant relative to the no-PN scenario, enabling reliable energy-based spatial detection using the no-PN threshold. PN-sensitivity and geometry-based metrics are introduced to design compact, PN-resilient MQAM symbol pools with low detection complexity. PN robustness is further improved through an enhanced PN-aware GRSM-MQAM system that exploits spatial modulation (SM) to recover part of the MQAM bits and strategically maps spatial-pattern Hamming weights to reduce the effective PN impact. In addition, a practical single-stage PN estimation/compensation architecture is proposed, while a benchmark double-stage compensation is adopted to quantify the upper bound achievable via separate Tx/Rx PN mitigation. Results show that under PN, the overall BER is mainly dominated by MQAM symbol detection errors, especially for denser constellations, whereas spatial detection remains robust. The proposed single-stage compensation improves PN resilience, while the benchmark double-stage compensation approaches near PN-free performance.

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arXiv2025

On the Ambiguity Function of OFDM-based ISAC Signals Under Non-Ideal Power Amplifiers

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for next-generation wireless and vehicular communications. Among candidate waveforms, Orthogonal Frequency Division Multiplexing (OFDM) has been extensively investigated over the past decade for its robustness against frequency-selective fading and its favorable ranging performance. However, the waveform's sensing and communication (S&C) performance depends strongly on the modulation scheme; while variable-amplitude constellations such as quadrature amplitude (QAM) are more efficient for communication, constant-modulus modulations such as phase shift keying (PSK) are more suitable for sensing. Yet, it remains unclear whether these findings persist under power amplifier (PA) nonlinearity. Because OFDM signals exhibit a high peak-to-average power ratio (PAPR), they require highly linear PAs to avoid distortion, which conflicts with radar requirements, where high transmit power is always beneficial for sensing. In this work, we analyze the effect of PA-induced distortions on the sensing task for PSK and QAM constellations. By introducing the Signal-to-Distortion Ratio (SDR), we examine the extent of the distortion limitation on the ranging task. We complement simulation results with a theoretical characterization of the ambiguity function (AF), thereby explicitly demonstrating how distortion artifacts manifest in the zero-Doppler sidelobes (i.e, ranging sidelobes) and the zero-delay sidelobes. Simulations show that PA distortions impose a palpable performance ceiling for both constellations, reshape the AF, and reduce detection probability, diminishing the theoretical advantage of unimodular signaling and further compromising the OFDM sensing performance with non-uniform envelope signals.

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arXiv2025

Explainable AI for Enhancing Efficiency of DL-based Channel Estimation

The support of artificial intelligence (AI) based decision-making is a key element in future 6G networks, where the concept of native AI will be introduced. Moreover, AI is widely employed in different critical applications such as autonomous driving and medical diagnosis. In such applications, using AI as black-box models is risky and challenging. Hence, it is crucial to understand and trust the decisions taken by these models. Tackling this issue can be achieved by developing explainable AI (XAI) schemes that aim to explain the logic behind the black-box model behavior, and thus, ensure its efficient and safe deployment. Recently, we proposed a novel perturbation-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications. The core idea of the XAI-CHEST framework is to identify the relevant model inputs by inducing high noise on the irrelevant ones. This manuscript provides the detailed theoretical foundations of the XAI-CHEST framework. In particular, we derive the analytical expressions of the XAI-CHEST loss functions and the noise threshold fine-tuning optimization problem. Hence the designed XAI-CHEST delivers a smart input feature selection methodology that can further improve the overall performance while optimizing the architecture of the employed model. Simulation results show that the XAI-CHEST framework provides valid interpretations, where it offers an improved bit error rate performance while reducing the required computational complexity in comparison to the classical DL-based channel estimation.

cs.AI↗

arXiv2023

Towards Explainable AI for Channel Estimation in Wireless Communications

Research into 6G networks has been initiated to support a variety of critical artificial intelligence (AI) assisted applications such as autonomous driving. In such applications, AI-based decisions should be performed in a real-time manner. These decisions include resource allocation, localization, channel estimation, etc. Considering the black-box nature of existing AI-based models, it is highly challenging to understand and trust the decision-making behavior of such models. Therefore, explaining the logic behind those models through explainable AI (XAI) techniques is essential for their employment in critical applications. This manuscript proposes a novel XAI-based channel estimation (XAI-CHEST) scheme that provides detailed reasonable interpretability of the deep learning (DL) models that are employed in doubly-selective channel estimation. The aim of the proposed XAI-CHEST scheme is to identify the relevant model inputs by inducing high noise on the irrelevant ones. As a result, the behavior of the studied DL-based channel estimators can be further analyzed and evaluated based on the generated interpretations. Simulation results show that the proposed XAI-CHEST scheme provides valid interpretations of the DL-based channel estimators for different scenarios.

cs.AI↗

arXiv2011

A New Model for Interference Analysis in Asynchronous Multi-Carrier Transmission

Interference at the radio receiver is a key source of degradation in quality of service of wireless communication systems. This paper presents a unified framework for OFDM/FBMC interference characterization and analysis in asynchronous environment. Multi-user interference is caused by the timing synchronization errors which lead to the destruction of the orthogonality between subcarriers. In this paper, we develop a theoretical analysis of the asynchronous interference considering the multi-path effects on the interference signal. We further propose an accurate model for interference that provides a useful computational tool in order to evaluate the performance of an OFDM/FBMC system in a frequency selective fading environment. Finally, simulation results confirmed the accuracy of the proposed model.

cs.NI↗