A Multimodal Predictive Channel Model Based on Dual-Camera Images for IIoT Communications

With the development of the sixth-generation (6G) wireless communications and Industrial Internet of Things (IIoT), numerous sensors, smart devices, and mobile terminals will be deployed in factories. Accurate modeling of IIoT channels facilitates the design and evaluation of the communication system, thereby ensuring the stability and efficiency of IIoT production systems. However, the huge data volume, the high dynamism, and complexity of IIoT communication environments pose significant challenges for traditional channel models in accurately capturing their characteristic variations. To address these issues, a deep-learning (DL)-based multimodal fusion predictive channel model is proposed in this article. The model is designed with the convolutional neural network (CNN) and multilayer perceptron (MLP) to extract feature information from input dual-camera images and basic physical parameters, respectively, and utilizes the fused features to predict received power and root-mean-square delay spread (RMS DS) in factory environments. The model integrates multiple modal information, capable of fully capturing the complex mapping relationship between the environment and channel characteristics. Comprehensive experiments demonstrate that the model achieves optimal prediction performance when integrating image information from both the transmitter (Tx) and receiver (Rx) simultaneously. Compared to several existing methods, our proposed predictive model exhibits superior performance. It presents a practical and feasible solution for the design and optimization of advanced IIoT systems in the future.