Self-Organizing Maps for advanced learning in cognitive radio systems

Modern everyday life keeps making wireless communications more and more popular. The wireless communications landscape is highly varying and its success depends on the efficient provision of a physically limited natural source namely, radio spectrum. Cognitive radio systems (CRSs) have been proposed as a very promising technology for addressing this situation by facilitating more flexible and intelligent spectrum management. However, the processes of a CRS are often proved to be rather arduous and time consuming. Accordingly, a learning mechanism, capable of building knowledge to the system can speed up the whole cognition process. Framed within this statement, this paper introduces and evaluates a mechanism which is based on the well-known unsupervised learning technique, called Self-Organizing Maps (SOMs), and is used for assisting a CRS to predict the raw data rate that can be obtained, when it senses specific input data from its environment. Results show that the proposed method can provide predictions which are correct up to a percentage of 78.9% while exhibiting performance comparable to other supervised neural network-based learning schemes.

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► We propose a mechanism for predicting the data rate in a Cognitive Radio System.
► The proposed mechanism is based on Self-Organizing Maps (SOMs).
► SOMs are a type of neural network that is trained using unsupervised learning.
► The SOM-based mechanism is evaluated against supervised learning schemes.
► The SOM-based mechanism can provide correct predictions up to a percentage of 78.9%.