Abstract

Predicting signal blockages in millimetre waves (mmWave) and terahertz (THz) networks is a challenging task that requires anticipating environmental changes. One promising solution is to use multi-modal data, such as vision and wireless inputs, and deep learning. However, combining these data sources can lead to higher communication costs, inefficient bandwidth usage, and undesirable latency, making it challenging. This paper proposes a semantic aware federated blockage prediction (SFBP) framework for a vision-aided next-generation wireless network. This framework uses computer vision techniques to extract semantic information from images and performs distributed on-device learning to enhance blockage prediction. Federated learning enables collaborative model training without exposing private data. Our proposed framework achieves 97.5% accuracy in predicting signal blockages, which is very close to the performance of centralised training. By using semantic information to train models, SFBP reduces communication costs by 88.75% and 57.87% compared to centralised learning and federated learning without semantics, respectively. On-device inference further reduces latency by 23% and 18% compared to centralised and federated learning without semantics, respectively.