Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

Abstract

The recent phenomena of tremendous growth in wireless communication application urge increasing need of radio spectrum, albeit it being a precious but natural resource. The recent technology under development to overview the situation is the concept of Cognitive Radio (CR). Recently the Artificial Intelligence (AI) tools are being considered for the topic. AI is the core of the cognitive engine that examines the external and internal environment parameters that leads to some postulations for QoS improvement. In this article, we propose a new Artificial Neural Network (ANN) model for detection of a spectrum hole. The model is trained with some pertinent features over a channel like SNR, channel capacity, bandwidth efficiency etc. The channel capacity status could be identified in a quantized index form. Some simulation results are presented.

Modeling and prediction primary nodes in wireless networks of cognitive radio using recurrent neural networks

“…From Figure 2, it is generally observed that the scientific literature bases the representation of the activity of the primary users with methodologies that have an important computational cost such as [13], [14], [15], [16] among others, being unviable in applications of open field when the conservation of energy is important [11]. An alternative, which could solve these shortcomings by increasing efficiency, are models based on self-learning that provide feedback from their own mistakes to enhance future performance, as is the case with RNNs.…”

An optimised neural network-based spectrum prediction scheme for cognitive radio

“…Therefore, this structure may also be used further for the prediction of the output in such types of environments. An ANN has also been used for making the CR to be able to learn from that of the environment and take a decision (Pattanayak et al, 2013).…”