Cognitive Radio Networking.pdf

Cognitive Radio-based Industrial Internet of Ad-hoc Sensor Networks (CR-IIAHSN) face challenges like varying industrial network topology, data routing, and spectrum availability. To address these issues, a cross-layer design concept is proposed, combining network and medium access control (MAC) layer functionalities using Reinforcement Learning (RL) algorithms. The RL-based technique, Q-learning, enables CR Secondary Users (SUs) to sense, learn, and make optimal decisions. The proposed RLCLD scheme improves SU network performance by up to 30% compared to conventional methods.

Key contributions include:

1. Developing a spectrum-aware routing protocol in the network layer to discover suitable network paths and adjust to dynamic CR-IIAHSN characteristics.
2. Modeling the routing method as a reinforcement learning task to acquire the most appropriate route.
3. Developing effective spectrum sensing and dynamic channel selection techniques for CR nodes using RL in the MAC layer.
4. Designing a cross-layer approach between the network and MAC layers using AI and machine learning technologies, such as RL and Q-learning.
5. Deriving a suitable RL model and comparative performance analysis through simulation results.

The proposed cross-layer design improves network performance by enabling better interaction, coordination, and joint optimization of different protocols. The use of RL algorithms allows CR SUs to learn from their environment and make optimal decisions, resulting in improved network performance and reliability.