The proposed joint communication and computation framework can be extended to support more advanced semantic communication techniques, such as multi-modal semantic information extraction and transmission, by incorporating additional computational resources and algorithms.
To support multi-modal semantic information extraction, the framework can be enhanced with machine learning models capable of processing and extracting information from various data types, such as images, text, and audio. This would involve integrating deep learning algorithms, natural language processing techniques, and computer vision models into the system to enable the extraction of semantic information from different modalities.
Furthermore, the framework can be expanded to include multi-modal data fusion techniques, where information from different modalities is combined to provide a more comprehensive understanding of the data. This would involve developing algorithms that can effectively fuse information from diverse sources and modalities to enhance the semantic communication process.
Additionally, the framework can be extended to support multi-modal transmission, where the extracted semantic information from different modalities is transmitted efficiently to the intended recipients. This would involve optimizing the transmission process to accommodate the diverse nature of the data and ensure reliable and efficient communication across different modalities.
By incorporating these advanced techniques into the framework, it can be tailored to handle complex semantic communication scenarios involving multi-modal data, enabling more sophisticated and efficient communication in industrial IoT applications.