# Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks

## TL;DR

This paper introduces a VQVAE-based multimodal data fusion method that effectively compresses and reconstructs diverse data types, including in 5G scenarios, with minimal performance loss and low computational requirements.

## Contribution

It develops a simple, effective VQVAE-based model for multimodal data fusion and extends it to 5G CSI feedback, enabling efficient data compression for wireless communication.

## Key findings

- High-quality reconstruction of paired MNIST-SVHN and WiFi spectrogram data
- Effective data compression in 5G CSI feedback with minimal performance loss
- Learned discriminative feature space for various input data types

## 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.