Abstract

The management of future networks is expected to fully exploit cognitive capabilities that embrace knowledge and intelligence, increasing the degree of automation, making the network more self-autonomous and enabling a personalized user experience. In this context, this paper presents the use of knowledge-based capabilities through a specific lab experiment focused on the Channel Selection functionality for Cognitive Radio Networks (CRN). The selection is based on a supervised classification that allows estimating the number of interfering sources existing in a given frequency channel. Four different classifiers are considered, namely decision tree, neural network, naive Bayes and Support Vector Machine (SVM). Additionally, a comparison against other channel selection strategies using Q-learning and game theory has also been performed. Results obtained in an illustrative and realistic test scenario have revealed that all the strategies allow identifying an optimum solution. However, the time to converge to this solution can be up to 27 times higher according to the algorithm selected.

Highlights

  • The increasing traffic demand will lead future wireless networks to face a severe shortage of spectrum, especially when considering the highly dense deployments of small cells envisaged for meeting the demands of future systems

  • The experiment focuses on the Channel Selection functionality for Cognitive Radio Networks (CRN), so that an access point decides the most appropriate channel to use within a band that is shared among multiple transmitters

  • This paper has presented an experiment focusing on the channel selection functionality for Cognitive Radio Networks (CRN), so that an access point decides the most appropriate channel to use within a band that is shared among multiple transmitters

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Summary

Introduction

The increasing traffic demand will lead future wireless networks to face a severe shortage of spectrum, especially when considering the highly dense deployments of small cells envisaged for meeting the demands of future systems. More solid results and conclusions can be derived from implementing such mechanisms in realistic conditions In this respect, WiSHFUL is a European project from the European Horizon 2020 Programme that focuses on speeding up the development and testing cycles of wireless solutions and, it offers a great opportunity to gain access to realistic data and measurements [7]. A comparison against other channel selection strategies using Q-learning and game theory has been performed In this way, this experiment contributes to expand the capabilities of the existing WiSHFUL Intelligence framework [9] that offers an experimentation environment for early implementation and validation of end-to-end 5G solutions that improve resource utilization through advanced reconfigurability of radio and network settings.

The IRIS testbed
Experimenting Channel Selection functionality using the IRIS testbed
Learning interference characterisation
Channel Selection
Results
Algorithm 1
Algorithm 2
Algorithm 3
Conclusions
Full Text
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