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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Abdelkader Hadjoudja
Ibn Tofail University
Computer Science & IT Electrical & Electronics Engineering

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Designing common-source low noise amplifier utilizing GaN HEMT for sub-6 GHz in 5G wireless applications Samia Zarrik; Abdelhak Bendali; Fatehi ALtalqi; Karima Benkhadda; Sanae Habibi; Zahra Sahel; Mouad El Kobbi; Abdelkader Hadjoudja; Mohamed Habibi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.26324

Abstract

In the domain of gallium nitride based high electron mobility transistors (GaN HEMT), this work refines a class A low noise amplifier (LNA) tailored for fifth generation (5G) wireless applications within the sub-6 GHz band. Employing a common-source topology and leveraging GaN HEMT technology, the amplifier seamlessly achieves operation at 3.5 GHz. Simulations were conducted using Advanced Design System (ADS) software. The GaN HEMT transistor manifests noteworthy intrinsic and extrinsic characteristics, with a Vds of 6 V, Vgs of -1.56 V, and Id of 1024 mA. Through meticulous optimization within the [3.3-3.9] GHz frequency band, the GaN HEMT transistor attains an impressive maximum gain of 16.225 dB, coupled with a minimal low noise figure (NF) of 1.232 dB. Additionally, the amplifier showcases noteworthy power added efficiency (PAE) of approximately 60.527%. These exceptional attributes position the amplifier as highly suitable for sub-6 GHz and millimeter-wave applications across the extensive 5G spectrum. The investigation is centered on precisely situating the LNA as a pivotal catalyst for improving 5G network front-end performance. With a dedicated focus on frequencies below 6 GHz, the research not only addresses challenges but also pioneers’ advancements in 5G application LNA design, ultimately elevating the overall system performance.
1×2 microstrip patch antennas array for mm-waves 5G application Karima Benkhadda; Fatehi ALtalqi; Abdelhak Bendali; Abderrahim Haddad; Samia Zarrik; Sanae Habibi; Zahra Sahel; Mohamed Habibi; Abdelkader Hadjoudja
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.26263

Abstract

In this paper we present the design of an antenna array for 5G applications. The proposed prototype of the antenna array is design to function at both 24 GHz and 27 GHz frequencies, utilizing Rogers RT5880 with a permittivity equal to 2.2 and a loss tangent of 0.0009. The CST Studio Suite software is employed for simulating the suggested. The primary goals of this research encompass achieving a notable return loss, increased gain, minimized voltage standing wave ratio (VSWR), enhanced directivity, and an overall improvement in operational efficiency. The results of the simulation showcase encouraging performance metrics, including a return loss of -68.70 dB, a bandwidth larger 7.369 GHz (ranging from 22.191 GHz to 29.56 GHz), a gain of 10.52 dB. Furthermore, the microstrip patch antennas (MPA) array system showcases an impressive efficiency rating of 95.63%.
Deep learning–based real time speed limit sign detection with YOLOv12 on edge AI platforms for embedded ADAS Mohammed Chaman; Anas El Maliki; Youssef Natij; Hamad Dahou; Abdelkader Hadjoudja
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11128

Abstract

This research examined a real-time speed-limit sign detection framework based upon deep learning using the YOLOv12 neural network, optimized for the use of small edge devices that are embedded advanced driver assistance systems (ADAS). You only look once version 12 (YOLOv12) achieved a remarkable detection performance, while maintaining efficient computation, utilizing significantly optimized lightweight attention modules with an R-ELAN backbone capable of small and partially occluded detection. A custom dataset comprising 23,000 annotated images was prepared and augmented to ensure robustness under varying conditions. Model training utilized quantization-aware techniques and optimization via TensorRT and ONNX Runtime. Deployment and performance were rigorously evaluated on resource-constrained edge platforms, specifically NVIDIA Jetson Nano and Raspberry Pi 5. Experimental results demonstrated exceptional detection performance, achieving a precision of 99.0%, recall of 99.1%, and mean average precision (mAP@50) of 99.2%, confirming YOLOv12’s suitability for reliable, real-time ADAS implementation in intelligent transportation and autonomous vehicles.
Co-Authors Abdelhak Bendali Abderrahim Haddad Anas El Maliki Fatehi ALtalqi Hamad Dahou Karima Benkhadda Mohamed Habibi Mohammed Chaman Mouad El Kobbi Samia Zarrik Sanae Habibi Youssef Natij Zahra Sahel