Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
This provides a solution for efficient multimodal data fusion in wireless communication and sensor networks, though it appears incremental as it adapts an existing VQVAE architecture to new domains.
The paper tackles multimodal data fusion by proposing a Vector-Quantized Variational Autoencoder (VQVAE) approach, achieving excellent reconstruction on MNIST-SVHN and WiFi spectrogram data and implementing it in a 5G CSI feedback system to compress data without significant performance loss.
This paper presents a novel approach for multimodal data fusion based on the Vector-Quantized Variational Autoencoder (VQVAE) architecture. The proposed method is simple yet effective in achieving excellent reconstruction performance on paired MNIST-SVHN data and WiFi spectrogram data. Additionally, the multimodal VQVAE model is extended to the 5G communication scenario, where an end-to-end Channel State Information (CSI) feedback system is implemented to compress data transmitted between the base-station (eNodeB) and User Equipment (UE), without significant loss of performance. The proposed model learns a discriminative compressed feature space for various types of input data (CSI, spectrograms, natural images, etc), making it a suitable solution for applications with limited computational resources.