Streamlining Multimodal Data Fusion in Wireless Communication and Sensor Networks

--This paper presents a novel approach for multi-modal data fusion based on the V ector-Quantized V ariational Autoencoder (VQV AE) 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 VQV AE 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. Multimodal fusion is an important aspect of modern artificial intelligence and machine learning systems. It is a process of combining data from multiple sensors to create a comprehensive understanding of the environment. In various applications, such as robotics, autonomous vehicles, and Internet of Things (IoT), multiple sensors are used to capture information from the environment, including vision, audio, lidar, radar, sonar, GPS and more. By combining this data, a more accurate and robust representation of the environment can be created. Multimodal sensor fusion is important because it helps to overcome the limitations of individual sensors and allows for more reliable and robust decision-making. However, compression of multimodal data is also needed for increasing efficiency, decreasing the cost of storage and transmission, and facilitating real-time processing of substantial datasets in a variety of applications. For example, in 5G networks, Channel State Information (CSI) feedback plays a critical role in the communication system.