Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks
Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks
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DOI：
10.1109/access.2020.3007871
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Das, Abir
中科院分区：
文献类型：
--
作者：
Ahmad, Rizwana;Soltani, Mohammad Dehghani;Das, Abir
光保真（LiFi）是利用发光二极管（led）进行高速无线通信的新兴通信技术。由于其巨大的未经许可的带宽，LiFi能够支持高数据速率。由于房间内的干扰、墙壁反射或障碍物，LiFi信道的质量会出现波动。另一方面，WiFi是另一种无线通信技术，它能够提供适中的数据速率和无处不在的覆盖范围。由于LiFi的电磁频谱与WiFi不重叠，因此两者可以共存，形成LiFi和WiFi的混合网络，实现无缝、高吞吐量的连接。混合系统的性能在很大程度上取决于接入点（AP）的分配和资源分配策略。本文考虑了一个由1个WiFi AP和4个LiFi AP组成的下行混合系统，并采用强化学习（RL）算法来确定最优的AP分配策略，以最大化系统的长期吞吐量，同时保证所需的用户公平性和满意度。在此基础上，研究了用户均匀分布和非均匀分布两种基于随机航路点模型的场景。所提出的系统的性能与最先进的基准方法进行了比较，例如信号强度策略（SSS），穷举搜索和迭代优化方法。根据平均系统吞吐量、用户满意度、公平性和容量中断概率来报告结果。结果表明，本文提出的强化学习方法在较低的复杂度下更接近穷举搜索方案。在大多数情况下，RL方法也优于SSS方案和迭代算法。
Light fidelity (LiFi) is an emerging communication technology, which utilizes the light-emitting diodes (LEDs) for high-speed wireless communications. Due to its huge unlicensed bandwidth, LiFi is capable of supporting high data rates. The quality of the LiFi channel fluctuates across the room due to interference, reflection from walls or blockage. On the other hand, WiFi is another wireless communication technology that is capable of providing moderate data rates with ubiquitous coverage. As the electromagnetic spectrum of LiFi does not overlap with WiFi, both of them can coexist to form a hybrid LiFi and WiFi network for seamless and high-throughput connectivity. The performance of a hybrid system significantly depends upon the access point (AP) assignment and resource allocation strategies. In this paper, a downlink hybrid system with one WiFi AP and four LiFi APs is considered, and a reinforcement learning (RL) algorithm is implemented in order to determine an optimal AP assignment strategy, which maximizes the long-term system throughput while ensuring the required users fairness and satisfaction. Furthermore, two different scenarios based on the random waypoint model with uniform and non-uniform distribution of users have been studied. The performance of the proposed system is compared against state-of-the-art benchmark approaches e.g., signal strength strategy (SSS), exhaustive search, and an iterative optimization method. The results are reported in terms of the average system throughput, user satisfaction, fairness, and capacity outage probability. It is shown that the proposed RL method performs closer to the exhaustive search scheme at fairly low complexity. The RL method also outperforms the SSS scheme and the iterative algorithm in most scenarios.