Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks

• URL: http://arxiv.org/abs/2302.12636v1
• Date: Fri, 24 Feb 2023 13:55:33 GMT
• Title: Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
• Authors: Mohammud J. Bocus, Xiaoyang Wang, Robert. J. Piechocki
• Abstract summary: 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.
• Score: 4.132799233018846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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