Open Access

ARTICLE

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

by Tauqeer Safdar Malik^1,*, 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:

(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. https://doi.org/10.32604/iasc.2022.021128

Received 24 June 2021; Accepted 05 August 2021; Issue published 08 February 2022

Abstract

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.

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