Load Balancing of Hybrid LiFi WiFi Networks Using Reinforcement learning
Load Balancing of Hybrid LiFi WiFi Networks Using Reinforcement learning
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DOI：
10.1109/pimrc48278.2020.9217382
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Rizwana Ahmad;Mohammad Dehghani Soltani;M. Safari;A. Srivastava
中科院分区：
文献类型：
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作者：
Rizwana Ahmad;Mohammad Dehghani Soltani;M. Safari;A. Srivastava
光保真（LiFi）是一种新兴的通信技术，其利用光强度调制以便将数据从发光二极管（LED）传输到用户。由于可见光光谱广阔，LiFi可以支持高数据速率;然而，其覆盖范围有限。与LiFi相比，WiFi工作在射频中，能够以有限的数据速率提供无处不在的覆盖。由于LiFi的频谱与WiFi不重叠，两者可以共存，形成混合LiFi和WiFi网络。混合LiFi和WiFi网络的优点是它提供了高数据速率和更好的连接。混合LiFi和WiFi网络的性能在很大程度上取决于负载均衡策略。因此，在本文中，基于梯度下降的强化学习（RL）已被提出来确定一个最佳的接入点（AP）的分配政策，旨在最大限度地提高平均网络吞吐量，同时确保用户的满意度。所提出的方法的性能进行比较，然后对传统的信号强度策略（SSS）的平均网络吞吐量，用户满意度和中断概率的结果。基于结果，据观察，所提出的RL方法提供了一个显着的改善，在所有的性能指标的SSS为基础的方法。
Light fidelity (LiFi) is an emerging communication technology that utilizes light intensity modulation in order to transfer data from light-emitting diode (LED) to users. Due to the vast visible light spectrum, LiFi can support high data rates; however, its coverage is limited. In contrast to LiFi, WiFi works in radio frequency and is capable of providing ubiquitous coverage with limited data rates. Since the spectrum of LiFi does not overlap with WiFi, both can co-exist to form a hybrid LiFi and WiFi network. The advantage of hybrid LiFi and WiFi network is that it provides high data rates and better connectivity. The performance of a hybrid LiFi and WiFi network significantly depends upon the load balancing strategies. Therefore, in this paper, gradient descent-based reinforcement learning (RL) has been proposed to determine an optimal access point (AP) assignment policy that aims to maximize the average network throughput while ensuring user’s satisfaction. The performance of the proposed method is then compared against conventional signal strength strategy (SSS); the results are presented in terms of the average network throughput, user satisfaction, and outage probability. Based on the results, it was observed that the proposed RL method provides a significant improvement in all the performance metrics over the SSS based method.