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In this paper, we propose a double deep Q-network (DDQN) based algorithm for resource optimization and data rate enhancement in a hybrid wireless fidelity (WiFi) and light fidelity (LiFi) communication system. Multiple LiFi and a WiFi access points (APs) have been developed in indoor settings. LiFi APs can deliver exceptionally high data rates while also serving as sources of illumination. However, LiFi APs alone fail to satisfy the throughput demands due to their small cell area and non-line-of-sight (NLOS) situation inefficiency. Therefore, in practical applications, WiFi APs, which can provide widespread coverage, can be integrated with LiFi network to maintain seamless connectivity across the system. Optimal resource allocation remains a crucial issue in these systems. Simulations results verify that proposed DDQN based optimal resource allocation outperforms existing DQN learning based algorithm by 26.7% with approximately 37.5% less power usage.

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Deep Reinforcement Learning Based Q-Networks for Efficient Resource Allocation in Hybrid Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Tanya Verma,
  • Shivanshu Shrivastava,
  • Arif Raza,
  • Umakant Dhar Dwivedi

摘要

In this paper, we propose a double deep Q-network (DDQN) based algorithm for resource optimization and data rate enhancement in a hybrid wireless fidelity (WiFi) and light fidelity (LiFi) communication system. Multiple LiFi and a WiFi access points (APs) have been developed in indoor settings. LiFi APs can deliver exceptionally high data rates while also serving as sources of illumination. However, LiFi APs alone fail to satisfy the throughput demands due to their small cell area and non-line-of-sight (NLOS) situation inefficiency. Therefore, in practical applications, WiFi APs, which can provide widespread coverage, can be integrated with LiFi network to maintain seamless connectivity across the system. Optimal resource allocation remains a crucial issue in these systems. Simulations results verify that proposed DDQN based optimal resource allocation outperforms existing DQN learning based algorithm by 26.7% with approximately 37.5% less power usage.