arXiv subjects

Mohammadreza Amini

Publications and source records attributed to Mohammadreza Amini.

12 recordsLinked to original sources

arXiv2026

Evaluating the Vergence-Accommodation Conflict in Gaze-Based 3D Target Selection

State-of-the-art head-mounted displays (HMDs) enable gaze-based selection in virtual environments. Yet, these HMDs suffer from the vergence-accommodation conflict (VAC), which is known to affect interaction performance. The VAC might influence gaze-based selection performance because it directly affects eye-movement behavior. Thus, in this paper, we investigate how the VAC influences gaze-based 3D target selection across varying depth conditions. Our results show that as the (visual) depth increases, user performance significantly decreases with gaze-based selection. Moreover, a previously suggested Variation in Diopter Fitts' law model captured this performance change better relative to a linear model. These findings provide evidence that gaze-based pointing is negatively affected by the VAC and highlight the importance of accounting for depth-dependent factors when designing gaze-based interaction in 3D environments.

cs.HC↗

arXiv2026

Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection

Jamming attacks are proliferating and pose a significant threat to the security of 5G and beyond networks. These attacks target 5G radio frequency (RF) domain and can disrupt the communication in wireless networks. While conventional machine learning and deep learning approaches demonstrate its potential for jamming detection, they typically require centralized data collection, compromising the privacy of user equipment (UEs). This work proposes a federated learning (FL)-based jamming detection framework that operates on over-the-air In-phase and Quadrature (IQ) samples extracted from Synchronization Signal Blocks (SSBs) in the RF domain. The framework enables collaborative model training across multiple UEs without sharing raw RF signal data. We adopt Federated Averaging (FedAvg) algorithm to train a 1D convolutional neural network (1DCNN) for effective detection of attacks. Numerical results demonstrate that the proposed FL framework achieves 97% accuracy and 97% F1-score, outperforming centralized baselines including MLP, 1DCNN, SVM, and logistic regression, while preserving the data privacy of all participating UEs

cs.NI↗

arXiv2026

Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine

All applications in fifth-generation (5G) networks rely on stable radio-frequency (RF) environments to support mission-critical services in mobility, automation, and connected intelligence. Their exposure to intentional interference or low-power jamming threatens availability and reliability, especially when such attacks remain below link-layer observability. This paper investigates lightweight, explainable, and hardware-efficient jamming detection using the Convolutional Tsetlin Machine (CTM) operating directly on 5G Synchronization Signal Block (SSB) features. CTM formulates Boolean logic clauses over quantized inputs, enabling bit-level inference and deterministic deployment on FPGA fabrics. These properties make CTM well suited for real-time, resource-constrained edge environments anticipated in 5G. The proposed approach is experimentally validated on a real 5G testbed using over-the-air SSB data, emulating practical downlink conditions. We benchmark CTM against a convolutional neural network (CNN) baseline under identical preprocessing and training pipelines. On the real dataset, CTM achieves comparable detection performance (Accuracy 91.53 +/- 1.01 vs. 96.83 +/- 1.19 for CNN) while training $9.5\times$ faster and requiring 14x less memory (45~MB vs.\ 624~MB). Furthermore, we outline a compact FPGA-oriented design for Zybo~Z7 (Zynq-7000) and provide resource projections (not measured) under three deployment profiles optimized for latency, power, and accuracy trade-offs. The results show that the CTM provides a practical, interpretable, and resource-efficient alternative to conventional DNNs for RF-domain jamming detection, establishing it as a strong candidate for edge-deployed, low-latency, and security-critical 5G applications while laying the groundwork for B5G systems.

eess.SP↗

arXiv2025

Reliable Task Offloading in MEC through Transmission Diversity and Jamming-Aware Scheduling

Mobile Edge Computing (MEC) enables low-latency applications by bringing computation closer to the user, but dynamic task arrivals and communication threats like jamming complicate reliable task offloading and resource allocation. In this paper, we formulate a dynamic MEC framework considering the transmission diversity that jointly addresses task scheduling and resource block (RB) assignment in the presence of jamming. First, we define and evaluate key network metrics-including dropped task ratio and bandwidth utilization-while maintaining service continuity by accounting for the existing commitments of the edge server to previously offloaded tasks. Then, we propose a jamming-aware offloading and RB allocation framework that leverages transmission diversity and optimal scheduling across distributed gNBs. The proposed solution is compared to a similar scenario without transmission diversity and two baseline strategies of first-come-first-served (FCFS) and shortest task first (STF). The proposed algorithm effectively mitigates the impact of jamming while enhancing resource utilization and minimizing task drop rates, making it highly suitable for mission-critical MEC applications. At signal-to-jamming-and-noise ratio (SJNR) of 4 dB, the proposed method achieves a $0.26$ task drop rate, outperforming the scenario without transmission diversity with a task drop rate of 0.50 and STF and FCFS strategies with 0.52 and 0.63 task drop rates, respectively.

cs.NI↗

arXiv2025

Fast and Adaptive Task Management in MEC: A Deep Learning Approach Using Pointer Networks

Task offloading and scheduling in Mobile Edge Computing (MEC) are vital for meeting the low-latency demands of modern IoT and dynamic task scheduling scenarios. MEC reduces the processing burden on resource-constrained devices by enabling task execution at nearby edge servers. However, efficient task scheduling remains a challenge in dynamic, time-sensitive environments. Conventional methods -- such as heuristic algorithms and mixed-integer programming -- suffer from high computational overhead, limiting their real-time applicability. Existing deep learning (DL) approaches offer faster inference but often lack scalability and adaptability to dynamic workloads. To address these issues, we propose a Pointer Network-based architecture for task scheduling in dynamic edge computing scenarios. Our model is trained on a generated synthetic dataset using genetic algorithms to determine the optimal task ordering. Experimental results show that our model achieves lower drop ratios and waiting times than baseline methods, and a soft sequence accuracy of up to 89.2%. Our model consistently achieves inference times under 2 seconds across all evaluated task counts, whereas the integer and binary programming approaches require approximately up to 18 seconds and 90 seconds, respectively. It also shows strong generalization across varying scenarios, and adaptability to real-time changes, offering a scalable and efficient solution for edge-based task management.

cs.NI↗

arXiv2025

CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets

In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise (AWGN) into the real-world In-phase and Quadrature (I/Q) OFDM datasets. A Convolutional Autoencoder (CAE) is exploited to implement a jamming detection over various characteristics such as heterogenous I/Q datasets; extracting relevant information on Synchronization Signal Blocks (SSBs), and fewer SSB observations with notable class imbalance. Given the characteristics of datasets, balanced datasets are acquired by employing a Conv1D conditional Wasserstein Generative Adversarial Network-Gradient Penalty(CWGAN-GP) on both majority and minority SSB observations. Additionally, we compare the performance and detection ability of the proposed CAE model on augmented datasets with benchmark models: Convolutional Denoising Autoencoder (CDAE) and Convolutional Sparse Autoencoder (CSAE). Despite the complexity of data heterogeneity involved across all datasets, CAE depicts the robustness in detection performance of jammed signal by achieving average values of 97.33% precision, 91.33% recall, 94.08% F1-score, and 94.35% accuracy over CDAE and CSAE.

cs.CR↗

arXiv2025

DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs

The Synchronization Signal Block (SSB) is a fundamental component of the 5G New Radio (NR) air interface, crucial for the initial access procedure of Connected and Automated Vehicles (CAVs), and serves several key purposes in the network's operation. However, due to the predictable nature of SSB transmission, including the Primary and Secondary Synchronization Signals (PSS and SSS), jamming attacks are critical threats. These attacks, which can be executed without requiring high power or complex equipment, pose substantial risks to the 5G network, particularly as a result of the unencrypted transmission of control signals. Leveraging RF domain knowledge, this work presents a novel deep learning-based technique for detecting jammers in CAV networks. Unlike the existing jamming detection algorithms that mostly rely on network parameters, we introduce a double-threshold deep learning jamming detector by focusing on the SSB. The detection method is focused on RF domain features and improves the robustness of the network without requiring integration with the pre-existing network infrastructure. By integrating a preprocessing block to extract PSS correlation and energy per null resource elements (EPNRE) characteristics, our method distinguishes between normal and jammed received signals with high precision. Additionally, by incorporating of Discrete Wavelet Transform (DWT), the efficacy of training and detection are optimized. A double-threshold double Deep Neural Network (DT-DDNN) is also introduced to the architecture complemented by a deep cascade learning model to increase the sensitivity of the model to variations of signal-to-jamming noise ratio (SJNR). Results show that the proposed method achieves 96.4% detection rate in extra low jamming power, i.e., SJNR between 15 to 30 dB. Further, performance of DT-DDNN is validated by analyzing real 5G signals obtained from a practical testbed.

eess.SP↗

arXiv2025

Joint Task Offloading and User Scheduling in 5G MEC under Jamming Attacks

In this paper, we propose a novel joint task offloading and user scheduling (JTO-US) framework for 5G mobile edge computing (MEC) systems under security threats from jamming attacks. The goal is to minimize the delay and the ratio of dropped tasks, taking into account both communication and computation delays. The system model includes a 5G network equipped with MEC servers and an adversarial on-off jammer that disrupts communication. The proposed framework optimally schedules tasks and users to minimize the impact of jamming while ensuring that high-priority tasks are processed efficiently. Genetic algorithm (GA) is used to solve the optimization problem, and the results are compared with benchmark methods such as GA without considering jamming effect, Shortest Job First (SJF), and Shortest Deadline First (SDF). The simulation results demonstrate that the proposed JTO-US framework achieves the lowest drop ratio and effectively manages priority tasks, outperforming existing methods. Particularly, when the jamming probability is 0.8, the proposed framework mitigates the jammer's impact by reducing the drop ratio to 63%, compared to 89% achieved by the next best method.

cs.CR↗

arXiv2025

A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks

Cyber-security for 5G networks is drawing notable attention due to an increase in complex jamming attacks that could target the critical 5G Radio Frequency (RF) domain. These attacks pose a significant risk to heterogeneous network (HetNet) architectures, leading to degradation in network performance. Conventional machine-learning techniques for jamming detection rely on centralized training while increasing the odds of data privacy. To address these challenges, this paper proposes a decentralized two-stage federated learning (FL) framework for jamming detection in 5G femtocells. Our proposed distributed framework encompasses using the Federated Averaging (FedAVG) algorithm to train a Convolutional Autoencoder (CAE) for unsupervised learning. In the second stage, we use a fully connected network (FCN) built on the pre-trained CAE encoder that is trained using Federated Proximal (FedProx) algorithm to perform supervised classification. Our experimental results depict that our proposed framework (FedAVG and FedProx) accomplishes efficient training and prediction across non-IID client datasets without compromising data privacy. Specifically, our framework achieves a precision of 0.94, recall of 0.90, F1-score of 0.92, and an accuracy of 0.92, while minimizing communication rounds to 30 and achieving robust convergence in detecting jammed signals with an optimal client count of 6.

cs.CR↗

arXiv2025

Active RIS-Assisted URLLC NOMA-Based 5G Network with FBL under Jamming Attacks

In this paper, we tackle the challenge of jamming attacks in Ultra-Reliable Low Latency Communication (URLLC) within Non-Orthogonal Multiple Access (NOMA)-based 5G networks under Finite Blocklength (FBL) conditions. We introduce an innovative approach that employs Reconfigurable Intelligent Surfaces (RIS) with active elements to enhance energy efficiency while ensuring reliability and meeting latency requirements. Our approach incorporates the traffic model, making it practical for real-world scenarios with dynamic traffic loads. We thoroughly analyze the impact of blocklength and packet arrival rate on network performance metrics and investigate the optimal amplitude value and number of RIS elements. Our results indicate that increasing the number of RIS elements from 4 to 400 can improve signal-to-jamming-plus-noise ratio (SJNR) by 13.64\%. Additionally, optimizing blocklength and packet arrival rate can achieve a 31.68% improvement in energy efficiency and reduced latency. These findings underscore the importance of optimized settings for effective jamming mitigation.

cs.CR↗

arXiv2024

The impact of mobility, beam sweeping and smart jammers on security vulnerabilities of 5G cells

The vulnerability of 5G networks to jamming attacks has emerged as a significant concern. This paper contributes in two primary aspects. Firstly, it investigates the effect of a multi-jammer on 5G cell metrics, specifically throughput and goodput. The investigation is conducted within the context of a mobility model for user equipment (UE), with a focus on scenarios involving connected vehicles (CVs) engaged in a mission. Secondly, the vulnerability of synchronization signal block (SSB) components is examined concerning jamming power and beam sweeping. Notably, the study reveals that increasing jamming power beyond 40 dBm in our specific scenario configuration no longer decreases network throughput due to the re-transmission of packets through the hybrid automatic repeat request (HARQ) process. Furthermore, it is observed that under the same jamming power, the physical downlink shared channel (PDSCH) is more vulnerable than the primary synchronization signal (PSS) and secondary synchronization signal (SSS). However, a smart jammer can disrupt the cell search process by injecting less power and targeting PSS-SSS or physical broadcast channel (PBCH) data compared to a barrage jammer. On the other hand, beam sweeping proves effective in mitigating the impact of a smart jammer, reducing the error vector magnitude root mean square from 51.59% to 23.36% under the same jamming power.

cs.CR↗

arXiv2024

Bypassing a Reactive Jammer via NOMA-Based Transmissions in Critical Missions

Wireless networks can be vulnerable to radio jamming attacks. The quality of service under a jamming attack is not guaranteed and the service requirements such as reliability, latency, and effective rate, specifically in mission-critical military applications, can be deeply affected by the jammer's actions. This paper analyzes the effect of a reactive jammer. Particularly, reliability, average transmission delay, and the effective sum rate (ESR) for a NOMA-based scheme with finite blocklength transmissions are mathematically derived taking the detection probability of the jammer into account. Furthermore, the effect of UEs' allocated power and blocklength on the network metrics is explored. Contrary to the existing literature, results show that gNB can mitigate the impact of reactive jamming by decreasing transmit power, making the transmissions covert at the jammer side. Finally, an optimization problem is formulated to maximize the ESR under reliability, delay, and transmit power constraints. It is shown that by adjusting the allocated transmit power to UEs by gNB, the gNB can bypass the jammer effect to fulfill the 0.99999 reliability and the latency of 5ms without the need for packet re-transmission.

cs.CR↗