Architecture for AI-Enabled Multimodal Semantic Communication and Distributed Computing

In this paper, we propose novel architecture supporting artificial intelligence-based multimodal semantic communication and distributed computing. The proposed architecture is built on existing concepts in adopted mobile networks, including cloud radio access network (C-RAN) and open-RAN (O-RAN). On top of current architectures, we introduce new key features including block for artificial intelligence (AI) training models for semantic encoding and decoding, semantic modules, and hierarchical edge cloud for distributed and parallel processing. Then, we formulate a delay minimization problem for processing of semantically encoded tasks by the hierarchical edge cloud. First, we derive optimal closed-form solutions for splitting the tasks between individual tiers of the hierarchical edge cloud while assuming actual communication and computing queues. Second, we propose a low-complexity algorithm selecting place, where the individual tasks are processed while adopting the optimal splitting of the tasks. Via simulations, we demonstrate that the proposed solution decreases average processing time and energy consumption due to computing by up to 50 % and 23 % when compared to the best performing state-of-the-art scheme.