Construction of Improved Device-to-Device Communication in 5G Networks based on Deep Learning Techniques

Sajad Ali Zearah¹, Ahmed R. Hassan^2,*, Aqeel Ali³, Saad Qasim Abbas⁴, Tamarah Alaa Diame⁵, Ahmed Mollah Khan⁶, Mariok Jojoal⁷

¹Scientific Research Center, Al-ayen University, Thi-Qar, Iraq

²Department of Medical Devices Engineering Technologies, National University of Science and Technology, Dhi Qar, Nasiriyah, Iraq

³Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq

⁴Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq

⁵Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq

⁶Department of Computer Engineering, University of Massachusetts Dartmouth, MA 02747Inst, USA

⁷Department of Computer Science and Engineering, University of Deusto, 48007 Bilbao, Spain

Emails: sajad@alayen.edu.iq; Ahmed.r.hassan@nust.edu.iq; Aqeel Ali@uoalfarahidi.edu.iq; saad.qasim@turath.edu.iq;  Tamarah. Alaa @ Kunoozu . Edu .Iq; ahmed.khan@umassd.edu;

marjoj@@deusto.es

Abstract

Device-to-Device (D2D) Communication promises outstanding data speeds, overall system capacity, and spectrum and energy efficiency without base stations and conventional network infrastructures, and these improvements in network performance sparked a lot of D2D research that exposed substantial challenges before being used to their fullest extent in 5G networks. This study suggests using Deep Learning-based Improved D2D communication (DLID2DC) in 5G networks to address these issues. Reprocessing resources between Cellular User Equipment (CUE) and D2D User Equipment (DUE) can increase system capacity without endangering the CUEs. The D2D resource allocation method allows for a flexible distribution of available resources across CUEs. In addition, several CUEs can consume the same pool of resources simultaneously. Researchers utilize various deep learning techniques to handle the difficulty of constructing D2D links and addressing their interference, mainly when using millimeter-wave (mmWave), to improve the performance of D2D networks. This research aims to increase system capacity by optimizing resource allocation using the suggested DLID2DC paradigm. The model uses Deep Learning methods to overcome interference issues and make D2D link building more efficient, especially in mmWave communication. The model uses Convolutional Neural Networks (CNNs) to learn and adapt to complicated D2D communication patterns, improving performance and dependability. The experimental findings show that, compared to other conventional approaches, the proposed DLID2DC model improves connection with lower end-to-end delay, energy efficiency, throughput, and efficient convergence time.

Keywords: Device-to-Device Communication; 5G Networks; Deep Learning; Convolutional Neural Network.