Active Channel Selection by Sensors using Artificial Neural Networks

Efficient channel selection is essential for optimizing resource utilization in wireless communication. Traditional static allocation often results in underutilization and wastage of channels. This research addresses these inefficiencies by using cognitive sensors, known as secondary users (SUs), to dynamically identify and utilize available channels, thereby minimizing channel wastage and deficits. The proposed strategy configures sensors cognitively to detect real-time channel availability. Secondary users (SUs) identify free channels initially allocated to primary users (PUs) and list these channels based on parameters such as capacity, transmission range, data load, and distance. An Active Channel Selection Network (ACSN) using artificial neural networks is employed to evaluate and allocate the optimal channel based on multiple parameters and sensor queue levels. This cognitive approach significantly reduces network channel deficits and wastage by dynamically detecting and utilizing free channels, ensuring more efficient channel usage. The ACSN improves the quality of channel selection, ensuring optimal allocation even when multiple channels are available. This method effectively addresses the challenges of channel underutilization and wastage in wireless communication networks, optimizing resource usage and enhancing overall network efficiency and performance.