Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks

Mohammud J. Bocus, Xiaoyang Wang, Robert. J. Piechocki
2/24/2023
cs.LGcs.AIeess.SP

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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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Cite this paper

@article{bocus2023streamlining,
  title  = {Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks},
  author = {Mohammud J. Bocus and Xiaoyang Wang and Robert. J. Piechocki},
  year   = {2023},
  eprint = {2302.12636},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.12636v1}
}

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