  This article introduces a new method to improve the dependability of
millimeter-wave (mmWave) and terahertz (THz) network services in dynamic
outdoor environments. In these settings, line-of-sight (LoS) connections are
easily interrupted by moving obstacles like humans and vehicles. The proposed
approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages
radar measurements and federated learning (FL) to train a dual-output neural
network (NN) model capable of simultaneously predicting blockage status and
time. This enables determining the optimal point for proactive handover (PHO)
or beam switching, thereby reducing the latency introduced by 5G new radio
procedures and ensuring high quality of experience (QoE). The framework employs
radar sensors to monitor and track objects movement, generating range-angle and
range-velocity maps that are useful for scene analysis and predictions.
Moreover, FL provides additional benefits such as privacy protection,
scalability, and knowledge sharing. The framework is assessed using an
extensive real-world dataset comprising mmWave channel information and radar
data. The evaluation results show that RaDaR substantially enhances network
reliability, achieving an average success rate of 94% for PHO compared to
existing reactive HO procedures that lack proactive blockage prediction.
Additionally, RaDaR maintains a superior QoE by ensuring sustained high
throughput levels and minimising PHO latency.
