Vision-Assisted Proactive Handover and Resource Allocation in Hybrid Lifi/Rf 6g Networks Using Edge AI

Light fidelity (LiFi) offers large unlicensed bandwidth for indoor 6G access, but its line-of-sight links are interrupted whenever a person walks between a luminaire and a device, or when the device tilts out of the receiver field of view. Handover schemes in hybrid LiFi/RF networks react only after the link is lost and therefore leave users without service for the detection and execution time of the handover. In this paper, we use the cameras that already monitor many smart buildings to anticipate these outages. An edge-AI pipeline tracks every person, converts the scene geometry into link-level features, fuses them with the measured signal-to-noise ratio, and predicts the probability that each LiFi link becomes unusable within the next second. A deep reinforcement learning agent, pre-trained from demonstrations and fine-tuned online, uses this forecast to select the access point of every user, while proportional-fair time sharing allocates resources inside each cell. In a room with six users and up to fifteen walking pedestrians, the learned vision-radio predictor raises the area under the ROC curve for 200-ms outage prediction from 0.79 (Kalman/Monte-Carlo forecast) to 0.89, and the proposed controller reduces link-interruption time by 42-47% with respect to a standard event-A3 handover, with no loss in mean throughput. Without retraining, the controller also reduces interruption by 53% when the pedestrians follow real trajectories from the ETH/UCY datasets.