Enhancement in 6G Intelligent Network Technologies using Cognitive Radio Network

The foundational principles of emerging wireless networks such as 5G, cognitive radio, and the prospective Internet have already integrated a multitude of prerequisites from the realm of the Internet of Things (IoT). The utilization of 6G technology opens up the possibility of conducting surgeries from a remote location. The rapid and efficient transmission of medical data can be enabled by the substantial data rates, minimal latency, and viability inherent in the 6G cognitive radio network, thereby potentially elevating both the quality of healthcare and its accessibility. The enhancement of Quality of Service (QoS) crucially depends on resolving the intricacies associated with routing Secondary Users (SUs) in a multi-hop fashion. To cater to the decentralized nature of Cognitive Radio Networks (CRN) within the context of a 6G-oriented Internet of Things (IoT) infrastructure, it becomes imperative to formulate solutions for routing SUs that do not hinge on cooperative mechanisms. The present routing protocols predominantly focus on defining an interference range for SUs, aimed at mitigating user interference. However, these limitations primarily consider interference with Primary Users (PUs) and do not align with the fulfillment of regulations set forth by the Federal Communication Commission (FCC). To address these challenges, a proactive method employs non-cooperative reinforcement learning techniques to systematically formulate IoT network routing strategies enhanced by 6G and cognitive radio capabilities. This pursuit introduces a novel Q-learning algorithm that modifies the existing Ad-hoc on-Demand Distance Vector Routing protocol (AODV), enhancing the reconfiguration process.