Reinforcement Learning Based Load Balancing for Hybrid LiFi WiFi Networks

Rizwana Ahmad*, Mohammad Dehghani Soltani, Majid Safari, Anand Srivastava, Abir Das

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Article number9136703
Pages (from-to)132273-132284
Number of pages12
JournalIEEE Access
Early online date8 Jul 2020
Publication statusE-pub ahead of print - 8 Jul 2020

Keywords / Materials (for Non-textual outputs)

  • Hybrid LiFi WiFi
  • light fidelity (LiFi)
  • load balancing
  • reinforcement learning (RL)
  • trust region policy optimization (TRPO)


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