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Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network

Tauqeer Safdar Malik1,*, Kaleem Razzaq Malik1, Muhammad Sanaullah2, Mohd Hilmi Hasan3, Norshakirah Aziz3

1 Air University Multan Campus, Department of Computer Science, Multan, 60000, Pakistan
2 Bahauddin Zakariya University, Department of Computer Science, Multan, 60000, Pakistan
3 Centre for Research in Data Science, Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia

* Corresponding Author: Tauqeer Safdar Malik. Email: email

(This article belongs to the Special Issue: Artificial Intelligence Techniques for Joint Sensing and Localization in Future Wireless Networks)

Intelligent Automation & Soft Computing 2022, 33(2), 809-824.


Cognitive Radio Network (CRN) has turn up to solve the issue of spectrum congestion occurred due to the wide spread usage of wireless applications for 6G based Internet of Things (IoT) network. The Secondary Users (SUs) are allowed to access dynamically the frequency channels owned by the Primary Users (PUs). In this paper, we focus the matter of contention of routing in multi hops setup by the SUs for a known destination in the presence of PUs. The traffic model for routing is generated on the basis of Poison Process of Markov Model. Every SU requires to reduce the end-to-end delay and packet loss of its transmission simultaneously to improve the data rate for the Quality of Service (QoS) of the Secondary Users. The issue of routing is formulated as stochastic learning process of non-cooperative games for the transformation of routing decisions of SUs. We propose a distributed non-cooperated reinforcement learning based solution for solving the issue of dynamic routing that can avert user interferences and channel interferences between the competing Sus in 6G-IoT network. The proposed solution combines and simulate the results to show the effectiveness and working of the proposed solution in decreasing the end-to-end delay, packet loss while meeting the average data rate requirement of QoS for SUs.


Cite This Article

APA Style
Malik, T.S., Malik, K.R., Sanaullah, M., Hasan, M.H., Aziz, N. (2022). Non-cooperative learning based routing for 6g-iot cognitive radio network. Intelligent Automation & Soft Computing, 33(2), 809-824.
Vancouver Style
Malik TS, Malik KR, Sanaullah M, Hasan MH, Aziz N. Non-cooperative learning based routing for 6g-iot cognitive radio network. Intell Automat Soft Comput . 2022;33(2):809-824
IEEE Style
T.S. Malik, K.R. Malik, M. Sanaullah, M.H. Hasan, and N. Aziz "Non-Cooperative Learning Based Routing for 6G-IoT Cognitive Radio Network," Intell. Automat. Soft Comput. , vol. 33, no. 2, pp. 809-824. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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