Adaptive Hybrid Optical RF Transmission Based on Deep Reinforcement Learning for Ultra-Reliable Low Latency Communication

Stella N. Arinze; Isidore U. Uche; Augustine O. Nwajana

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arXiv · 2610.04728

Adaptive Hybrid Optical RF Transmission Based on Deep Reinforcement Learning for Ultra-Reliable Low Latency Communication

Stella N. Arinze·Isidore U. Uche·Augustine O. Nwajana

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Abstract

Ultra Reliable Low-Latency Communication (URLLC) demands submillisecond latency and reliability above 99.999%, which are difficult to guarantee under dynamic wireless and atmospheric conditions. Although free space optical (FSO) links provide high-capacity transmission, their performance degrades under turbulence and fog, while radiofrequency (RF) links offer robustness but limited bandwidth. This paper presents a reinforcement learning-based hybrid optical RF framework that adaptively selects transmission modes to satisfy stringent URLLC requirements. The hybrid decision process is modeled as a Markov Decision Process and optimized using a Deep Q Network (DQN) controller. Extensive simulations under varying attenuation and fading scenarios demonstrate up to 35% latency reduction and 25 to 30 % throughput improvement compared to RF-only and static hybrid schemes, while maintaining reliability above 99.999%. A laboratory-scale testbed operating at 1550 nm and 28 GHz further validates the approach, showing less than 6% deviation between simulated and measured results. The proposed framework demonstrates the practical feasibility of intelligent hybrid transmission for mission-critical next generation wireless networks.

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BibTeX RIS

Stella N. Arinze, Isidore U. Uche, Augustine O. Nwajana. 2026-10-03. Adaptive Hybrid Optical RF Transmission Based on Deep Reinforcement Learning for Ultra-Reliable Low Latency Communication. https://arxiv.org/abs/2610.04728

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