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A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks

机译:LTE网络中基于强化学习的小区间干扰协调

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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.
机译:作为蜂窝网络的长期演进网络由于所使用的传输信道(即空中)的性质而遭受许多损害。小区间干扰是长期演进网络面临的主要损害,因为它使用一种频率复用方案,其中在每个小区中使用整个带宽。在本文中,我们为下行长期演进网络提出了一种没有带宽划分的全动态小区间干扰协调方案。我们使用强化学习方法。所提出的方案是联合资源分配和功率分配方案,其目的是使长期演进网络中的小区间干扰最小化。与其他算法相比,所提出方案的性能在SINR,丢包和延迟方面显示了服务质量的提高。

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