Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866915106191835136 |
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| author | Bocus, Mohammud J. Wang, Xiaoyang Piechocki, Robert. J. |
| author_facet | Bocus, Mohammud J. Wang, Xiaoyang Piechocki, Robert. J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12636 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks Bocus, Mohammud J. Wang, Xiaoyang Piechocki, Robert. J. Machine Learning Artificial Intelligence Signal Processing 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. |
| title | Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks |
| topic | Machine Learning Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2302.12636 |