Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey

Journal article


Ahmad, R. Z., Rizwan, M., Yousuf, M. J., Khan, MB., Almadhor, A., Gadekallu, T. R. and Abbas S. 2025. Device-to-device communication in 5G heterogeneous network based on game-theoretic approaches: A comprehensive survey. Journal of Network and Computer Applications. 238. https://doi.org/10.1016/j.jnca.2025.104152
AuthorsAhmad, R. Z., Rizwan, M., Yousuf, M. J., Khan, MB., Almadhor, A., Gadekallu, T. R. and Abbas S.
Abstract

In the evolution of Fifth-Generation (5G) oriented wireless communication technology, the conventional
wireless communication performance indicators, including network capacity, spectrum efficiency, and Quality
of Services (QoS), need to be continuously improved to optimize the utilization of the wireless spectrum.
As a key candidate technology for 5G, Device-to-Device (D2D) communication improves system performance,
enhances user experience, and expands cellular communication applications. D2D communication provides a
better quality of services with minor communication delays, improving overall network performance, efficient
utilization of network resources, and enhanced network capacity by utilizing short-distance communication
between devices in close proximity. D2D communication technology has recently received widespread attention
due to its promising nature. This survey comprehensively reviews D2D communication and the techniques
involved in different phases of successful D2D communication. In addition, this survey paper also presents an
extensive review of proposed solutions based on game-theoretic approaches aiming to optimize the performance
of D2D communication in 5G. The major objectives of this survey paper are to thoroughly analyse current
developments in D2D communication and review game theory applications in D2D communication. This survey
also identifies challenges in D2D communication, opens issues, and suggests future research areas.

KeywordsGame theory; 5G networks; Device-to-device communication; Peer discovery; Mode selection; Interference management; Resources management; Power control
Year2025
JournalJournal of Network and Computer Applications
Journal citation238
PublisherElseiver
ISSN1095-8592
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jnca.2025.104152
Web address (URL)https://www.sciencedirect.com/science/article/pii/S1084804525000499
Accepted author manuscript
License
File Access Level
Restricted
Publisher's version
File Access Level
Restricted
Output statusPublished
Publication dates
Online03 Mar 2025
Publication process dates
Accepted18 Feb 2025
Deposited20 May 2025
Permalink -

https://repository.derby.ac.uk/item/qx2v7/device-to-device-communication-in-5g-heterogeneous-network-based-on-game-theoretic-approaches-a-comprehensive-survey

  • 22
    total views
  • 3
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches
Yonbawi, S., Afzal, A., Yasir, M., Rizwan, M. and Kryvinska, N. 2025. Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches. IEEE Access. 13, pp. 11248 - 11264. https://doi.org/10.1109/ACCESS.2025.3528214
Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems
Khan, A., Rizwan, M., Bagdasar, O., Alabdulatif, A., Alamro, S. and Alnajim, A. 2024. Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems. Computer Modeling in Engineering & Sciences . 141 (3), pp. 2121-2141. https://doi.org/10.32604/cmes.2024.054380
Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems
Abid, R., Rizwan, M., Alabdulatif, A., Alnajim, A., Alamro, M. and Azrour, M. 2024. Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems. CMC-Computers, Materials & Continua. pp. 1-17. https://doi.org/10.32604/cmc.2024.046880
Enhanced clustering based routing protocol in vehicular ad‐hocnetworks
Afia, N., Rizwan, M., Shtwai, A., Almadhor, A., Akhtaruzzaman, Md., Islam, S. and Rahman, H. 2023. Enhanced clustering based routing protocol in vehicular ad‐hocnetworks. IET Electrical Systems in Transportation. 13 (1), pp. 1-15. https://doi.org/10.1049/els2.12069
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions
Javed, A. R., Saadia, A., Mughal, H., Gadekallu, T.R., Rizwan, M., Maddikunta, P.K.R., Mahmud, M., Liyanage, M and Hussain, A. 2023. Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions. Cognitive Computation. pp. 1-46. https://doi.org/10.1007/s12559-023-10153-4
Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network
Alshahrani, H., Khan, A., Rizwan, M., Al Reshan, M. S., Sulaiman, A. and Shaikh, A. 2023. Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network. Sustainability. 15 (11), pp. 1-18. https://doi.org/10.3390/su15119001
Intelligent Transportation Systems in Smart City: A Systematic Survey
Hassan, M. A., Javed, R., Farhatullah, Granelli, F., Gen, X., Rizwan, M., Ali, S. H., Junaid, H. and Ullah, S. 2023. Intelligent Transportation Systems in Smart City: A Systematic Survey. 2023 International Conference on Robotics and Automation in Industry (ICRAI). IEEE Computer Society. https://doi.org/10.1109/ICRAI57502.2023.10089543
Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network
Rizwan, M., Aysha, S., Maryam, S., AR Javed, Baker, T. and Dhiya, O. 2022. Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network. IEEE Access. 10, pp. 29731 - 29740. https://doi.org/10.1109/ACCESS.2022.3153108
Exploratory Data Analysis, Classification, Comparative Analysis, Case Severity Detection, and Internet of Things in COVID 19 Telemonitoring for Smart Hospitals
Shabbir, A., Shabbir, M., Javed, A. R., Rizwan, M., Iwendi, C. and Chakraborty, C. 2022. Exploratory Data Analysis, Classification, Comparative Analysis, Case Severity Detection, and Internet of Things in COVID 19 Telemonitoring for Smart Hospitals. Journal of Experimental & Theoretical Artificial Intelligence. 35 (4), pp. 507-534. https://doi.org/10.1080/0952813X.2021.1960634