Genetic Algorithm Optimization of the Link Layer for Throughput Improvement in 5G NR Networks

Authors

  • Sulekha Pateriya School of Physical Sciences, Banasthali Vidyapith, Rajasthan, India
  • Shuvabrata Bandopadhaya School of Physical Sciences, Banasthali Vidyapith, Rajasthan, India
  • Soumya Ranjan Samal Technical University of Sofia, Sofia, Bulgaria; Silicon University, Odisha, India
  • Antoni Ivanov Technical University of Sofia, Sofia, Bulgaria
  • Vladmir Poulkov Technical University of Sofia, Sofia, Bulgaria

DOI:

https://doi.org/10.13052/jmm1550-4646.2126

Keywords:

5G NR, Network-link layer, Parameter optimization, Throughput maximization, Genetic Algorithm

Abstract

With the proliferation of the fifth generation (5G) wireless communications, adaptive resource allocation in various deployment scenarios remains a significant research topic. In this paper we propose a genetic algorithm (GA) based optimization of network-link layer parameters to improve the throughput in 5G New Radio (NR) networks. For achieving optimal system throughput while maintaining stringent quality of service (QoS) requirements for the block error rates (BLER) and signal-to-noise ratio (SNR), this work develops a mathematical model that incorporates the SNR, modulation and coding schemes (MCS), and hybrid automatic repeat request (HARQ) processes. This solution provides a robust foundation for the implementation of 5G NR networks in dynamic environments and arbitrary channel conditions. As a result, throughput of up to 240 Mbps is achieved. Multi-objective optimization, including energy efficiency and latency parameters, may be considered as future directions for exploration.

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Author Biographies

Sulekha Pateriya, School of Physical Sciences, Banasthali Vidyapith, Rajasthan, India

Sulekha Pateriya is currently working as a Research Scholar at the 5G Lab, Banasthali Vidyapith, Jaipur, where she has been pursuing her Ph.D. in Electronics and Communication Engineering since July 2022. Her doctoral research, focuses on throughput optimization, signal processing, wireless communication systems, and network reliability in 5G NR. She has done M.Sc.. in Electronics and Communication (2009) from DAVV University, Indore, a B.Ed. in Physics and Mathematics (2018), and a B.Sc. in Physics, Chemistry, and Mathematics (2007). With over 13 years of academic and mentoring experience, she has contributed to reputed institutions including Unacademy and Shiv Jyoti Group, Kota. Her research interests include AI-integrated wireless systems, 5G network optimization, and emerging communication technologies.

Shuvabrata Bandopadhaya, School of Physical Sciences, Banasthali Vidyapith, Rajasthan, India

Shuvabrata Bandopadhaya is currently working as an associate professor in the department of electronics at the School of Physical Sciences, Banasthali Vidyapith, Rajasthan. He has received his M.Tech. and Ph.D. degrees in communication systems specialisation from KIIT University, Bhubaneswar, India. He has nearly 20 years of experience in teaching and research at various reputed institutes and universities in India. His areas of research interest include wireless communication and networks, Internet of Things, and AI.

Soumya Ranjan Samal, Technical University of Sofia, Sofia, Bulgaria; Silicon University, Odisha, India

Soumya Ranjan Samal received his Ph.D. degree in Communication Networks, Faculty of Telecommunications from Technical University of Sofia at Sofia, Bulgaria. He received his B.Tech. degree in Electronics & Instrumentation Engineering from Biju Patnaik University of Technology, India in 2004. Soumya then went on to pursue his M.E. in Computer Science & Engineering from the Utkal University of Bhubaneswar, India in 2009. He, as an Associate Professor in Silicon University, India has acquired a solid experience about 18 years of teaching in Communication Engineering. Soumya also worked as a Project Engineer in Indian Institute of Technology, Bombay, India in 2005. His research area of interest includes, Interference Management in 5G cellular network, Green Communication movement to develop Energy Efficient solutions through antenna parameters and IoT.

Antoni Ivanov, Technical University of Sofia, Sofia, Bulgaria

Antoni Ivanov received the PhD degree in Communication Networks and Systems from the Technical University of Sofia (TUS), Bulgaria. He holds a Master degree in Innovative Communication Technologies and Entrepreneurship from TUS, and Aalborg University, Denmark in 2016. He is currently a Postdoctoral researcher at the “Teleinfrastructure Lab”, Faculty of Telecommunications, TUS. His research interests include cognitive radio networks, adaptive algorithms for dynamic spectrum access, deep learning-based solutions for cognitive radio applications, volumetric spectrum occupancy assessment, and graph signal processing for resource allocation in current and future wireless networks.

Vladmir Poulkov, Technical University of Sofia, Sofia, Bulgaria

Vladimir Poulkov has received the M.Sc. and Ph.D. degrees from the Technical University of Sofia (TUS), Sofia, Bulgaria. He has more than 30 years of teaching, research, and industrial experience in the field of Telecommunications. He has successfully managed numerous industrial, engineering, R&D and educational projects. He has been Dean of the Faculty of the Telecommunications at TUS and Vice Chairman of the General Assembly of the European Telecommunications Standardization Institute (ETSI). Currently the Head of the “Teleinfrastructure” R&D Laboratory at TUS and Chairman of Cluster for Digital Transformation and Innovation, Bulgaria. He is Fellow of the European Alliance for Innovation; Senior IEEE Member. He has authored many scientific publications and is tutoring BSc, MSc, and PhD courses in the field of Information Transmission Theory and Wireless Access Networks.

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Published

2025-07-08

How to Cite

Pateriya, S. ., Bandopadhaya, S. ., Samal, S. R. ., Ivanov, A. ., & Poulkov, V. . (2025). Genetic Algorithm Optimization of the Link Layer for Throughput Improvement in 5G NR Networks. Journal of Mobile Multimedia, 21(02), 343–362. https://doi.org/10.13052/jmm1550-4646.2126

Issue

2025: Vol 21 Iss 2

Section

Articles

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