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Future Internet 2019, 11(1), 19; https://doi.org/10.3390/fi11010019

A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks

1
Department of Computer Science and Telecommunications, National Advanced Engineering School, University of Maroua, 46 Maroua, Cameroon
2
Communication Networks, University of Bremen, 28359 Bremen, Germany
3
Department of Computer Science, Higher Teachers’ Training College, University of Maroua, 46 Maroua, Cameroon
4
Faculty of Science, University of Ngaoundere, 454 Ngaoundere, Cameroon
*
Authors to whom correspondence should be addressed.
Received: 6 December 2018 / Revised: 10 January 2019 / Accepted: 14 January 2019 / Published: 17 January 2019
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Abstract

Long Term Evolution networks, which are cellular networks, are subject to many impairments due to the nature of the transmission channel used, i.e. the air. Intercell interference is the main impairment faced by Long Term Evolution networks as it uses frequency reuse one scheme, where the whole bandwidth is used in each cell. In this paper, we propose a full dynamic intercell interference coordination scheme with no bandwidth partitioning for downlink Long Term Evolution networks. We use a reinforcement learning approach. The proposed scheme is a joint resource allocation and power allocation scheme and its purpose is to minimize intercell interference in Long Term Evolution networks. Performances of proposed scheme shows quality of service improvement in terms of SINR, packet loss and delay compared to other algorithms. View Full-Text
Keywords: intercell interference coordination; resource allocation; power allocation; reinforcement learning; genetic algorithm; optimization intercell interference coordination; resource allocation; power allocation; reinforcement learning; genetic algorithm; optimization
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Témoa, D.; Förster, A.; Kolyang; Doka Yamigno, S. A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks. Future Internet 2019, 11, 19.

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