A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks

Djorwé Témoa, Anna Förster, Kolyang and Serge Doka Yamigno
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Djorwé Témoa: Department of Computer Science and Telecommunications, National Advanced Engineering School, University of Maroua, 46 Maroua, Cameroon
Anna Förster: Communication Networks, University of Bremen, 28359 Bremen, Germany
Kolyang: Department of Computer Science, Higher Teachers’ Training College, University of Maroua, 46 Maroua, Cameroon
Serge Doka Yamigno: Faculty of Science, University of Ngaoundere, 454 Ngaoundere, Cameroon

Future Internet, 2019, vol. 11, issue 1, 1-23

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.

Keywords: intercell interference coordination; resource allocation; power allocation; reinforcement learning; genetic algorithm; optimization (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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