An experimental assessment of channel selection in cognitive radio networks

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 net-work, 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.Peer ReviewedPostprint (author's final draft

Game Theory-based Channel Selection for LTE-U

The Project intends to analyse the performance of a game theory-based channel selection in LTE-U.The main topic of this thesis project is the study of a channel selection strategy for LTE-U based on the game theory. The method consists on a repeated game where each small cell is a player with the purpose of finding the best channel where to set up the LTE-U carrier and it uses the ITEL-BA algorithm in order to make the system to converge to a Nash Equilibrium state. The aim is to evaluate the performance of the system in terms of achieved throughput and convergence time depending on the variation of some parameters, which are the exploration rate, the achieved throughput and the non-stationarity condition. The work environment consists of a software that simulate the scenario where several small cells apply this strategy

On modeling channel selection in LTE-U as a repeated game

This paper addresses the channel selection problem for Long Term Evolution Unlicensed (LTE-U). Channel selection is a frequency-domain mechanism that facilitates the coexistence of multiple networks sharing the unlicensed band. In particular, the paper considers a fully distributed approach where each small cell autonomously selects the channel to set-up an LTE-U carrier. The problem is modeled using a non-cooperative repeated game and the Iterative Trial and Error Learning - Best Action (ITEL-BA) learning algorithm is used to drive convergence towards a Nash Equilibrium. The proposed approach is evaluated by means of simulations in different situations analyzing both the throughput performance and the convergence behavior.Peer ReviewedPostprint (published version

On modeling channel selection in LTE-U as a repeated game

This paper addresses the channel selection problem for Long Term Evolution Unlicensed (LTE-U). Channel selection is a frequency-domain mechanism that facilitates the coexistence of multiple networks sharing the unlicensed band. In particular, the paper considers a fully distributed approach where each small cell autonomously selects the channel to set-up an LTE-U carrier. The problem is modeled using a non-cooperative repeated game and the Iterative Trial and Error Learning - Best Action (ITEL-BA) learning algorithm is used to drive convergence towards a Nash Equilibrium. The proposed approach is evaluated by means of simulations in different situations analyzing both the throughput performance and the convergence behavior.Peer Reviewe