Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 49 references that are in the map. First posted on arXiv 24 Feb 2023.
Builds on
19 of 49 references.
References · 19
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7 more
- 2020Gimme Signals: Discriminative signal encoding for multimodal activity recognitioncited by 18
- 2020Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMOcited by 14
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.