Federated Learning for Reliable mmWave Systems: Vision-Aided Dynamic Blockages Prediction

Line of sight (LoS) links that use high frequencies are sensitive to blockages, making it challenging to scale future ultra-dense networks (UDN) that capitalise on millimetre wave (mmWave) and potentially terahertz (THz) networks. This paper embraces two novelties; Firstly, it combines machine learning (ML) and computer vision (CV) to enhance the reliability and latency of next-generation wireless networks through proactive identification of blockage scenarios and triggering proactive handover (PHO). Secondly, this study adopts federated learning (FL) to perform decentralised model training so that data privacy is protected, and channel resources are conserved. Our vision-aided PHO framework localises users using object detection and localisation (ODL) algorithm that feeds a multiple-output neural network (NN) model to predict possible blockages. This involves analysing images captured from the video cameras co-located with the base stations (BSs) in conjunction with wireless parameters to predict future blockages and subsequently trigger PHO. Simulation results show that our approach performs remarkably well in highly dynamic multi-user environments where vehicles move at different speeds, and achieves 93.6% successful PHO. Furthermore, the proposed framework outperforms the reactive-HO methods by a factor of 3.3 in terms of latency while maintaining a high quality of experience (QoE) for the users.