Cell Outage Compensation Using Q-learning for Self-Organizing Networks

Salsabel Adel, K. Muhammed, Ahmed Y. Abdallah, M. Rida, A. Morsy, Gehad Nasser, Ahmed K. F. Khattab, A. Taha, Hany El-Akel
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引用次数: 1

Abstract

In this paper, we introduce a Q-learning-based algorithm for Cell Outage Compensation (COC) in Self Organizing Networks (SONs). The algorithm compensates the coverage in the outage area by modifying the power and antenna tilt angle parameters of the neighboring cells. The proposed Q-learning algorithm adapts the reward via learning the consequences of the taken actions to compensate the coverage gap, which guarantees a fully autonomous and accurate COC as we do not assume the knowledge of the propagation model or other models of the environment. This contrasts with existing COC approaches which are inaccurate as they assume the knowledge of the mathematical models of the system and solve the COC problem given such mathematical models. Simulation results show a 92% accessibility of the proposed Q-learning algorithm compared to 81% accessibility of existing approaches that are based on modelling the environment.
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基于q学习的自组织网络单元中断补偿
本文介绍了一种基于q学习的自组织网络(SONs)小区中断补偿(COC)算法。该算法通过修改相邻小区的功率和天线倾角参数来补偿中断区域的覆盖。提出的q -学习算法通过学习所采取行动的后果来适应奖励,以补偿覆盖差距,这保证了完全自主和准确的COC,因为我们不假设传播模型或环境的其他模型的知识。这与现有的COC方法形成对比,这些方法不准确,因为它们假设系统的数学模型的知识,并在给定这种数学模型的情况下解决COC问题。仿真结果表明,与基于环境建模的现有方法的81%的可访问性相比,所提出的Q-learning算法的可访问性为92%。
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