A Survey on Cognitive Radio Network using Artificial Neural Network
Adnan Waqar, Saima Khadim, Aamir Zeb, Samreen Amir, Imran Khan · International Journal of Future Generation Communication and Networking · 2017
The emergence of Internet of Things and other applications of wireless communication has resulted in increase of air interference among various wireless devices.In upcoming time we will be connecting more and more devices wirelessly.In addition to an increase in number of devices, many devices also demand higher bandwidth.We have a limited spectrum available for communication and as the demand increases it creates more and more congestion in the available spectrum.Besides this scarcity of spectrum, it has been observed that all available frequencies in this spectrum are not efficiently utilized.Some frequency bands face congestion while others are underutilized.The solution of all these issues is Cognitive Radio.The fundamental theory of cognitive radio deals with the issues mentioned above and provides efficient utilization of available spectrum.In cognitive radio when a frequency is not utilized by primary user (Licensed user), it is allocated to secondary user (Unlicensed user) who can use the frequency until there is no primary user.For searching primary and secondary user we use spectrum sensing.Depending on the type of users and the environment, this spectrum sensing can be a time consuming task which can severely impact the QoS.To deal with this critical issue we use machine learning techniques, which predict spectrum holes in an available frequency band.This in turn reduces spectrum sensing time and power consumed in sensing.Among various Machine learning techniques, Artificial Neural Networks is one of the most popular and widely used technique.Unlike other Machine learning Techniques, Neural Network doesn't require prior knowledge of the system and in most cases it doesn't require the model to be retrained an every instance.These advantages makes it one of the most popular technique for cognitive radios.So far a lot of work has been done on implementing Artificial Neural Network models for predicting the most suitable frequency for a secondary user.In this paper a comprehensive survey has been conducted on various ANN techniques, its comparison with other machine learning techniques and discussion on various learning models to increase the decision making ability of cognitive radio's cognitive engine.ANN uses supervised learning and this paper compares it with other supervised learning techniques (like SVM) and also unsupervised learning techniques and statistical models.The paper provides detailed knowledge about what factors influence the use of ANN in cognitive engines and under certain conditions which ANN technique is most suitable.