Using cognitive radio to deliver green communications: a reinforcement learning approach. Issue 4 (11th February 2020)

• Record Type: Journal Article
• Authors: Bendella, Mohammed Saleh; Benmammar, Badr; Krief, Francine
• Is Part Of: International journal of knowledge engineering and data mining. Volume 6:Issue 4(2019)
• Page Start: 331
• Page End: 355
• Publication Date: 2020-02-11
• Subjects: green networking -- cognitive radio -- energy efficiency -- artificial intelligence -- reinforcement learning -- knowledge base -- Markov decision process -- MDP; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

In this paper, we are interested in the concept of green networking and the solutions brought by cognitive radio technology in this field. The purpose of this work is to find a mechanism that minimises energy consumption by integrating it in a cognitive radio network. For this, we have used the Q-learning algorithm, a reinforcement learning technique that will help the cognitive users to find the optimal channel that has a low transmission power by guaranteeing the needs of their application and therefore a reduction in the energy consumption of their batteries while minimising interference in the network. The obtained results are very satisfactory because we have shown that through the integration of the Q-learning algorithm in a cognitive radio network, we have been able to significantly reduce the energy consumption and the interferences of the cognitive radio terminals and therefore we have satisfied the service of green networking.