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Amor Nafkha

Publications and source records attributed to Amor Nafkha.

15 recordsLinked to original sources

A Transformer for Joint Multi-Receiver Pilotless Wi-Fi Decoding

In this paper we discuss the development of a fully pilotless multi-access-point Wi-Fi receiver. This is based on a self-attention Transformer that operates on per-(access point, subcarrier) tokens and outputs bit-wise logits for a standard low-density parity-check decoder. In realistic ray-traced indoor channels, pilotless decoding becomes feasible as soon as some spatial diversity is available. While a single-access point configuration does not attain practical error rates, when compared to an ideal maximum-ratio-combining baseline with perfect channel knowledge, cooperative configurations with three access points achieve coded bit error rates of approximately $10^{-5}$. This is achieved while benefiting from higher spectral efficiency by eliminating pilots.

eess.SP

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.

eess.SP

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.

eess.SP

Scalable Cross-Attention Transformer for Cooperative Multi-AP OFDM Uplink Reception

We propose a cross-attention Transformer for joint decoding of uplink OFDM signals received by multiple coordinated access points. A shared per-receiver encoder learns the time-frequency structure of each grid, and a token-wise cross-attention module fuses the receivers to produce soft log-likelihood ratios for a standard channel decoder without explicit channel estimates. Trained with a bit-metric objective, the model adapts its fusion to per-receiver reliability and remains robust under degraded links, strong frequency selectivity, and sparse pilots. Over realistic Wi-Fi channels, it outperforms classical pipelines and strong neural baselines, often matching or surpassing a local perfect-CSI reference while remaining compact and computationally efficient on commodity hardware, making it suitable for next-generation coordinated Wi-Fi receivers.

eess.SP

Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks

Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks, particularly in the context of 5G and beyond (6G) systems. Within the context of GF-RA, this study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD). GF-RA addresses scalability by employing non-orthogonal pilot sequences, which provides an efficient alternative comparing to conventional grant-based random access (GB-RA) technique that are constrained by the scarcity of orthogonal preamble resources. In this paper, we propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC in cell-free massive multiple-input multiple-output (CF-mMIMO) networks. We propose two distinct framework deployment strategies, centralized and decentralized, both tailored to streamline the proposed approach implementation across network infrastructures. Moreover, we introduce optimized post-detection methodologies complemented by a clustering stage to enhance overall detection performances. Our 3GPP-compliant simulations have validated that the proposed algorithm achieves state-of-the-art model-based activity detection accuracy while significantly reducing complexity. Achieving 99% accuracy, it demonstrates real-world viability and effectiveness.

eess.SP

Attacking at non-harmonic frequencies in screaming-channel attacks

Screaming-channel attacks enable Electromagnetic (EM) Side-Channel Attacks (SCAs) at larger distances due to higher EM leakage energies than traditional SCAs, relaxing the requirement of close access to the victim. This attack can be mounted on devices integrating Radio Frequency (RF) modules on the same die as digital circuits, where the RF can unintentionally capture, modulate, amplify, and transmit the leakage along with legitimate signals. Leakage results from digital switching activity, so the hypothesis of previous works was that this leakage would appear at multiples of the digital clock frequency, i.e., harmonics. This work demonstrates that compromising signals appear not only at the harmonics and that leakage at non-harmonics can be exploited for successful attacks. Indeed, the transformations undergone by the leaked signal are complex due to propagation effects through the substrate and power and ground planes, so the leakage also appears at other frequencies. We first propose two methodologies to locate frequencies that contain leakage and demonstrate that it appears at non-harmonic frequencies. Then, our experimental results show that screaming-channel attacks at non-harmonic frequencies can be as successful as at harmonics when retrieving a 16-byte AES key. As the RF spectrum is polluted by interfering signals, we run experiments and show successful attacks in a more realistic, noisy environment where harmonic frequencies are contaminated by multi-path fading and interference. These attacks at non-harmonic frequencies increase the attack surface by providing attackers with an increased number of potential frequencies where attacks can succeed.

cs.CR

Multi-Screaming-Channel Attacks: Frequency Diversity for Enhanced Attacks

Side-channel attacks consist of retrieving internal data from a victim system by analyzing its leakage, which usually requires proximity to the victim in the range of a few millimetres. Screaming channels are EM side channels transmitted at a distance of a few meters. They appear on mixed-signal devices integrating an RF module on the same silicon die as the digital part. Consequently, the side channels are modulated by legitimate RF signal carriers and appear at the harmonics of the digital clock frequency. While initial works have only considered collecting leakage at these harmonics, late work has demonstrated that the leakage is also present at frequencies other than these harmonics. This result significantly increases the number of available frequencies to perform a screaming-channel attack, which can be convenient in an environment where multiple harmonics are polluted. This work studies how this diversity of frequencies carrying leakage can be used to improve attack performance. We first study how to combine multiple frequencies. Second, we demonstrate that frequency combination can improve attack performance and evaluate this improvement according to the performance of the combined frequencies. Finally, we demonstrate the interest of frequency combination in attacks at 15 and, for the first time to the best of our knowledge, at 30 meters. One last important observation is that this frequency combination divides by 2 the number of traces needed to reach a given attack performance.

cs.CR

Federated Learning for Secure and Efficient Device Activity Detection in mMTC Networks

Grant-free random access in massive machine-type communications enables low-latency connectivity with minimal signaling. However, sporadic device activation requires efficient device activity detection. We propose a federated learning-based device activity detection approach, leveraging distributed training to enhance security and privacy while maintaining low computational complexity. Compared to existing methods, our solution achieves competitive detection performance, addressing scalability and security challenges in mMTC networks.

eess.SP

Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments

Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. This sparse support recovery of device activity status is performed by a finite cluster of access points (APs) from a large number of geographically distributed APs collaborating to serve a larger number of devices. Active devices transmit non-orthogonal pilot sequences to APs, which forward the received signals to a central processing unit (CPU) for collaborative activity detection. This paper proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU. Furthermore, the study assesses the algorithm's robustness to input perturbations and examines the effects of adopting fixed-point representation on its performance.

eess.SP

Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO

Modern wireless networks must reliably support a wide array of connectivity demands, encompassing various user needs across diverse scenarios. Machine-Type Communication (mMTC) is pivotal in these networks, particularly given the challenges posed by massive connectivity and sporadic device activation patterns. Traditional grant-based random access (GB-RA) protocols face limitations due to constrained orthogonal preamble resources. In response, the adoption of grant-free random access (GF-RA) protocols offers a promising solution. This paper explores the application of supervised machine learning models to tackle activity detection issues in scenarios where non-orthogonal preamble design is considered. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks operating under GF-RA protocols. Additionally, this study presents a novel clustering strategy that simplifies and enhances activity detection accuracy, assesses the resilience of the algorithm to input perturbations, and investigates the effects of adopting floating-to-fixed-point conversion on algorithm performance. Simulations conducted adhere to 3GPP standards, ensuring accurate channel modeling, and employ a deep learning approach to boost the detection capabilities of mMTC GF-RA devices. The results are compelling: the algorithm achieves an exceptional 99\% accuracy rate, confirming its efficacy in real-world applications.

cs.LG

Virtual Triggering: a Technique to Segment Cryptographic Processes in Side Channel Traces

Side-Channel Attacks (SCAs) exploit data correla-tion in signals leaked from devices to jeopardize confidentiality. Locating and synchronizing segments of interest in traces from Cryptographic Processes (CPs) is a key step of the attack. The most common method consists in generating a trigger signal to indicate to the attacker the start of a CP. This paper proposes a method called Virtual Triggering (VT) that removes the need for the trigger signal and automates trace segmentation. When the time between repetitions is not constant, further trace alignment techniques are required. Building on VT, we propose a simple method to learn representative segment templates from a profiling device similar to the victim, and to automatically locate and pull out these segments from other victim devices using simple pattern recognition. We evaluate VT on screaming channel attacks [1], which initially used a Frequency Component (FC) known to appear at a single time in leaked signals, as a trigger to segment traces. We demonstrate that VT not only performs equivalently to FC on a standard attack scenario, but we also show how using VT with the automatic pullout technique improves the attack efficiency and enables more realistic attack scenarios. Thanks to VT, screaming channel attacks can now: (1) succeed with only half of the segments collected compared to the FC trigger from the original attack; and (2) absorb time variations between CPs.

cs.CR

Closed-Form Expressions of Ergodic Capacity and MMSE Achievable Sum Rate for MIMO Jacobi and Rayleigh Fading Channels

Multimode/multicore fibers are expected to provide an attractive solution to overcome the capacity limit of the current optical communication system. In the presence of high cross-talk between modes/cores, the squared singular values of the input/output transfer matrix follow the law of the Jacobi ensemble of random matrices. Assuming that the channel state information is only available at the receiver, we derive a new expression for the ergodic capacity of the MIMO Jacobi fading channel. This expression involves double integrals which can be evaluated easily and efficiently. Moreover, the method used in deriving this expression does not appeal to the classical one-point correlation function of the random matrix model. Using a limiting transition between Jacobi and Laguerre polynomials, we derive a similar formula for the ergodic capacity of the MIMO Rayleigh fading channel. Moreover, we derive a new exact closed form expressions for the achievable sum rate of MIMO Jacobi and Rayleigh fading channels employing linear minimum mean squared error (MMSE) receivers. The analytical results are compared to the results obtained by Monte Carlo simulations and the related results available in the literature, which shows perfect agreement.

cs.IT

Upper and Lower Bounds for the Ergodic Capacity of MIMO Jacobi Fading Channels

In multi-(core/mode) optical fiber communication, the transmission channel can be modeled as a complex sub-matrix of the Haar-distributed unitary matrix (complex Jacobi unitary ensemble). In this letter, we present new analytical expressions of the upper and lower bounds for the ergodic capacity of multiple-input multiple-output Jacobi-fading channels. Recent results on the determinant of the Jacobi unitary ensemble are employed to derive a tight lower bound on the ergodic capacity. We use Jensen's inequality to provide an analytical closed-form upper bound to the ergodic capacity at any signal-to-noise ratio (SNR). Closed-form expressions of the ergodic capacity, at low and high SNR regimes, are also derived. Simulation results are presented to validate the accuracy of the derived expressions.

cs.IT

New Lower Bound on the Ergodic Capacity of Optical MIMO Channels

In this paper, we present an analytical lower bound on the ergodic capacity of optical multiple-input multiple-output (MIMO) channels. It turns out that the optical MIMO channel matrix which couples the mt inputs (modes/cores) into mr outputs (modes/cores) can be modeled as a sub-matrix of a m x m Haar-distributed unitary matrix where m > mt,mr. Using the fact that the probability density of the eigenvalues of a random matrix from unitary ensemble can be expressed in terms of the Christoffel-Darboux kernel. We provide a new analytical expression of the ergodic capacity as function of signal-to-noise ratio (SNR). Moreover, we derive a closed-form lower-bound expression to the ergodic capacity. In addition, we also derive an approximation to the ergodic capacity in low-SNR regimes. Finally, we present numerical results supporting the expressions derived.

cs.IT

Accurate Measurement of Power Consumption Overhead During FPGA Dynamic Partial Reconfiguration

In the context of embedded systems design, two important challenges are still under investigation. First, improve real-time data processing, reconfigurability, scalability, and self-adjusting capabilities of hardware components. Second, reduce power consumption through low-power design techniques as clock gating, logic gating, and dynamic partial reconfiguration (DPR) capabilities. Today, several application, e.g., cryptography, Software-defined radio or aerospace missions exploit the benefits of DPR of programmable logic devices. The DPR allows well defined reconfigurable FPGA region to be modified during runtime. However, it introduces an overhead in term of power consumption and time during the reconfiguration phase. In this paper, we present an investigation of power consumption overhead of the DPR process using a high-speed digital oscilloscope and the shunt resistor method. Results in terms of reconfiguration time and power consumption overhead for Virtex 5 FPGAs are shown.

cs.AR