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Devi Rahmayanti
Universitas Gajayana
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Journal : Journal of Engineering, Electrical and Informatics

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Predicting Quality of Service on Cellular Networks Using Artificial Intelligence Devi Rahmayanti
Journal of Engineering, Electrical and Informatics Vol. 5 No. 2 (2025): June: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i2.3901

Abstract

The purpose of this research is to explore the application of artificial intelligence (AI) techniques, particularly Machine Learning, in predicting quality of service (QoS) on mobile networks, with the main focus being to test the ability of AI models to predict several QoS parameters, involving several important stages that reflect best practices in the development of artificial intelligence (AI)-based predictive systems for mobile networks. The dataset used in this study consists of data collected from simulations of mobile networks with various load and latency conditions. The parameters measured include Throughput, Latency and Packet Loss. Model evaluation was carried out to measure prediction performance using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) measurements. The AI models used include machine learning algorithms consisting of K-Nearest Neighbors (KNN) for classification and regression on QoS datasets, Support Vector Machine (SVM) to model non-linear relationships between QoS parameters and input variables, and Deep Learning (LSTM=Long Short-Term Memory) used to predict QoS based on time sequence data. This study found that LSTM-based deep learning models have the lowest prediction error rate in estimating packet loss, so they can provide the most accurate results in predicting QoS on mobile networks. This approach is capable of handling data that is sequential and has significant time dependence, making it more suitable for dynamic mobile network applications.
Beamforming Efficiency in MIMO-IRS Using Non-Convex Optimization Method Devi Rahmayanti
Journal of Engineering, Electrical and Informatics Vol. 5 No. 3 (2025): October: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i3.6722

Abstract

Advances in wireless communication technology face challenges in providing high channel capacity, energy efficiency, and transmitted signal quality in complex channels. Previous studies on Multiple Input Multiple Output (MIMO) with Intelligent Reflecting Surface (IRS) generally discuss theoretical models under ideal channel assumptions using the Semidefinite Relaxation (SDR) method, which exhibits high complexity and limited scalability. A research gap emerges due to the scarcity of studies on MIMO-IRS that address realistic optimization efficiency to maximize the Signal-to-Noise Ratio (SNR) in dynamic environments. This study aims to overcome these limitations through the integrated application of Alternating Optimization (AO) and Manifold Optimization (MO), which can handle non-convex problems more efficiently. The research is grounded in ontological, epistemological, and axiological aspects, employing experimental and simulation methods to optimize active beamforming at the Base Station and passive beamforming at the IRS while maintaining the unit modulus constraint. The results demonstrate that the AO-MO combination in MIMO-IRS increases channel capacity by up to 39.8%, SNR by up to 19.1%, and reduces computational time by more than fivefold compared to conventional methods without AO-MO. The contribution of this study lies in an optimization approach that efficiently enhances channel capacity and SNR without increasing computational complexity, enabling its application in wireless networks requiring high-speed and low-latency communication.
Co-Authors Agianto agus rahmat Apipah, Apipah Arisandi, Ervina Dwi Atika Asiah, Nor Atmaja, Meta Adiwinata Azlina, Fitri Ayatul Badaliyah, Nor Assyifa Baehaqi Bunga Indryan Noor Rindu Lestari Damayanti, Emmelia Astika F. Dhian Ririn Lestari, Dhian Ririn Endang Pertiwiwati Fitriana, Laila Noor Fitriani, Winda Herawati Herawati Herry Setiawan Huda, Radiyatul Juliana, Ima Khairunnisa Kurnia Rachmawati, Kurnia M. Yogi Riyantama Isjoni Magdalena, Sandra Barbara Moersito Wimbo Wibowo Muhammad Hafz Noor Diani Noviana, Fitria Nurlikasari, Anggelia Nurul Inayah Octovian, Reza Putri, Senna Qobita Dwi Rachmadani, Aldie Rahmawati, Emelia Ramadhan, Gilang Putra Rasyidah, Ani Rini Astuti Sa'dillah, Muhammad Refly Safitri, Eka Saraditha Santi, Eka Sari, Nor Laila Sari Setyowati, Anggi Sucianingsih, Herlina Sylvana, Mutia Tahlil, Teuku Tina Handayani Nasution