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Inicio  /  Future Internet  /  Vol: 11 Par: 1 (2019)  /  Artículo
ARTÍCULO
TITULO

A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks

Djorwé Témoa    
Anna Förster    
Kolyang and Serge Doka Yamigno    

Resumen

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.

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INFRAESTRUCTURA

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