Volume 10, Issue 6 (June 2023), Pages: 180-186
----------------------------------------------
Original Research Paper
Communication mode selection and game theoretic bandwidth sharing model for D2D relay communication
Author(s):
Syed Mohammad Abbas Zaidi 1, *, Aamir Zeb Shaikh 1, Asad Arfeen 2
Affiliation(s):
1Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi, Pakistan
2Department of Computers and Information Technology, NED University of Engineering and Technology, Karachi, Pakistan
Full Text - PDF XML
* Corresponding Author.
Corresponding author's ORCID profile: https://orcid.org/0009-0009-1209-129X
Digital Object Identifier:
https://doi.org/10.21833/ijaas.2023.06.021
Abstract:
Device-to-device (D2D) communication plays a crucial role in achieving successful implementation of 5G+ and 6G wireless networks. The selection of the communication mode is a vital parameter that enables the activation of a communication link through D2D relays. Consequently, this selection can be considered the fundamental functionality responsible for activating the communication mode of transmission within any device-to-device communication network. This research paper proposes a communication mode selection scheme based on a hexagonal cellular structure. The scheme holds significant potential for application in various wireless transmission schemes. Additionally, the paper investigates the issue of bandwidth sharing in device-to-device networks. In future wireless systems, device-centric approaches will be widely adopted, necessitating a key focus on spectrum sharing. The proposed scheme not only facilitates wireless users in sharing their available spectrum with others but also allows them to receive financial rewards in return. This cooperative sharing approach fosters collaboration among wireless users. Furthermore, the paper compares the performance of two utility functions for the purpose of bandwidth sharing. The Cobb-Douglas model is utilized to present the proposed bandwidth-sharing scheme between two users. Simulation experiments are conducted to determine the percentage of bandwidth shared by the two users under various scenarios, including a case where both users share 50% of the bandwidth. The results indicate that the optimal utility function is achieved when one user shares 10% of the bandwidth while the other user shares 90%.
© 2023 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: D2D relay, Mode selection, Cellular radio, Device-to-device communication, Cobb-Douglas
Article History: Received 3 October 2022, Received in revised form 15 March 2023, Accepted 2 May 2023
Acknowledgment
The authors would acknowledge NED University of Engineering and Technology for providing support to complete the research.
Compliance with ethical standards
Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Citation:
Zaidi SMA, Shaikh AZ, and Arfeen A (2023). Communication mode selection and game theoretic bandwidth sharing model for D2D relay communication. International Journal of Advanced and Applied Sciences, 10(6): 180-186
Permanent Link to this page
Figures
Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9
Tables
No Table
----------------------------------------------
References (25)
- Alam S, Sarfraz M, Usman MB, Ahmad MA, and Iftikhar S (2017). Dynamic resource allocation for cognitive radio based smart grid communication networks. International Journal of Advanced and Applied Sciences, 4(10): 76-83. https://doi.org/10.21833/ijaas.2017.010.012 [Google Scholar]
- Altaher A (2017). Hybrid approach for sentiment analysis of Arabic tweets based on deep learning model and features weighting. International Journal of Advanced and Applied Sciences, 4(8): 43-49. https://doi.org/10.21833/ijaas.2017.08.007 [Google Scholar]
- Amanuel SVA and Ameen SYA (2021). Device-to-device communication for 5G security: A review. Journal of Information Technology and Informatics, 1(1): 26-31. [Google Scholar]
- Amaonwu O, Matthew UO, Kazaure JS, Lawal M, Onyedibe ON, Nwamouh UC, and Nwanakwaugwu AC (2022). Adoption of android IoT smart technologies for rural agricultural innovation and implementation of green economy reforms. International Journal of Applied Agricultural Sciences, 8(4): 162-173. [Google Scholar]
- Bello O and Zeadally S (2014). Intelligent device-to-device communication in the Internet of Things. IEEE Systems Journal, 10(3): 1172-1182. https://doi.org/10.1109/JSYST.2014.2298837 [Google Scholar]
- Corson MS, Laroia R, Li J, Park V, Richardson T, and Tsirtsis G (2010). Toward proximity-aware internetworking. IEEE Wireless Communications, 17(6): 26-33. https://doi.org/10.1109/MWC.2010.5675775 [Google Scholar]
- Feng D, Lu L, Yuan-Wu Y, Li GY, Feng G, and Li S (2013). Device-to-device communications underlaying cellular networks. IEEE Transactions on Communications, 61(8): 3541-3551. https://doi.org/10.1109/TCOMM.2013.071013.120787 [Google Scholar]
- Gandotra P, Jha RK, and Jain S (2017). A survey on device-to-device (D2D) communication: Architecture and security issues. Journal of Network and Computer Applications, 78: 9-29. https://doi.org/10.1016/j.jnca.2016.11.002 [Google Scholar]
- Goldberger AS (1968). The interpretation and estimation of Cobb-Douglas functions. Econometrica: Journal of the Econometric Society, 35(3-4): 464-472. https://doi.org/10.2307/1909517 [Google Scholar]
- Jiang F, Wang B, and Sun C (2015). Communication mode selection and pricing mechanism for relaying based device-to-device communications. International Journal of Future Generation Communication and Networking, 8(5): 125-136. https://doi.org/10.14257/ijfgcn.2015.8.5.13 [Google Scholar]
- Khadim S, Waqar A, Zeb A, Khan I, and Hussain I (2017). Smart cognitive cellular network. International Journal of Future Generation Communication and Networking, 10(12): 23-34. https://doi.org/10.14257/ijfgcn.2017.10.12.03 [Google Scholar]
- Khan AA, Shaikh AZ, Naqvi S, and Altaf T (2016). Implementation of cooperative spectrum sensing algorithm using Raspberry Pi. International Journal of Advanced Computer Science and Applications, 7(12): 363–367. https://doi.org/10.14569/IJACSA.2016.071247 [Google Scholar]
- Khan MA, Shaikh AZ, Naqvi S, Khadim S, and Altaf T (2019). Deep learning enabled spectrum sensing radio for opportunistic usage. International Journal of Computer Science and Network Security, 19(11): 179. [Google Scholar]
- Khan S and Altayar M (2021). Industrial Internet of Things: Investigation of the applications, issues, and challenges. International Journal of Advanced and Applied Sciences, 8(1): 104-113. https://doi.org/10.21833/ijaas.2021.01.013 [Google Scholar]
- Letaief KB, Chen W, Shi Y, Zhang J, and Zhang YJA (2019). The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 57(8): 84-90. https://doi.org/10.1109/MCOM.2019.1900271 [Google Scholar]
- Lin X, Andrews JG, Ghosh A, and Ratasuk R (2014). An overview of 3GPP device-to-device proximity services. IEEE Communications Magazine, 52(4): 40-48. https://doi.org/10.1109/MCOM.2014.6807945 [Google Scholar]
- Noura M and Nordin R (2016). A survey on interference management for device-to-device (D2D) communication and its challenges in 5G networks. Journal of Network and Computer Applications, 71: 130-150. https://doi.org/10.1016/j.jnca.2016.04.021 [Google Scholar]
- Ramanathan R and Redi J (2002). A brief overview of ad hoc networks: Challenges and directions. IEEE Communications Magazine, 40(5): 20-22. https://doi.org/10.1109/MCOM.2002.1006968 [Google Scholar]
- Sankar S and Srinivasan P (2018). Energy and load aware routing protocol for Internet of Things. International Journal of Advanced and Applied Sciences, 7(3): 255-264. https://doi.org/10.11591/ijaas.v7.i3.pp255-264 [Google Scholar]
- Shaikh AZ and Altaf T (2013). Collaborative spectrum sensing under suburban environments. International Journal of Advanced Computer Science and Applications, 4(7): 62-65. https://doi.org/10.14569/IJACSA.2013.040709 [Google Scholar]
- Shaikh AZ and Tamil L (2015). Cognitive radio enabled telemedicine system. Wireless Personal Communications, 83: 765-778. https://doi.org/10.1007/s11277-015-2423-1 [Google Scholar]
- Srinivasulu A and Pushpa A (2020). Disease prediction in big data healthcare using extended convolutional neural network techniques. International Journal of Advanced and Applied Sciences, 9(2): 85-92. https://doi.org/10.11591/ijaas.v9.i2.pp85-92 [Google Scholar]
- Srinivasulu A, Barua T, Neelakantan U, and Nowduri S (2022). Early prediction of COVID-19 using modified convolutional neural networks. In: Satyanarayana C, Gao XZ, Ting CY, and Muppalaneni NB (Eds.), Machine learning and internet of things for societal issues: 69-82. Springer, Singapore, Singapore. https://doi.org/10.1007/978-981-16-5090-1_6 [Google Scholar]
- Wang M and Yan Z (2017). A survey on security in D2D communications. Mobile Networks and Applications, 22: 195-208. https://doi.org/10.1007/s11036-016-0741-5 [Google Scholar]
- Zellner A, Kmenta J, and Dreze J (1966). Specification and estimation of Cobb-Douglas production function models. Econometrica: Journal of the Econometric Society, 34(4): 784-795. https://doi.org/10.2307/1910099 [Google Scholar]
|