Precoder and Detector Learning for Vision-based mmWave Received Power Prediction

Multi-modal data collected from various sensors is instrumental in enhancing proactive handover management, beam directions and received powers prediction. However, what essential information to extract and how to effectively allocate wireless resources to transmit the information to a central processor (e.g., a base station (BS)) for decision making is a challenging task. In this work, we consider an uplink multi-user goal-oriented system, where images extracted from users' depth cameras reflect blockage status between users and their serving BS, which are then used for future received power prediction. In the system, we employ a convolutional neural network to learn a joint semantic source and channel encoder such that essential information is extracted from images. Subsequently, we model the multi-user subcarrier communication system as a hypergraph and use hyper-edge graph neural networks to learn precoders at the user side and detector at the BS side. Simulation results demonstrate that by jointly training a deep neural network-based encoder, decoder, precoder and detector, the communication system can achieve lower prediction errors than traditional precoder and detector, especially in low signal-to-noise ratio scenarios. We also show a trade-off between prediction performance and the computational complexity.