Cross-Modal Semantic Communications Over Wireless Emergency Networks

Wireless emergency networks play a crucial role in natural disasters, enabling seamless communication between explorers (e.g., rescue robots) and remote command centers. However, unstable communication links and limited computational resources hinder explorers from directly transmitting massive real-time content for remote human observation or locally computing the latest detection results. To address this challenge, this paper introduces a cross-modal semantic communications paradigm, where our highlights are characterized by precise semantic extraction, low-complexity implementation, and progressive semantic transmission. Specifically, by simplifying the desired task from human-oriented signal recovery to the machine-oriented decision, we firstly deploy a lightweight cross-modal semantic encoder on the explorer, which precisely extracts compact semantics relevant to the decision-making based on inter-modal correlations. Then, to mitigate the impact of intermittent network connections, we develop a scalable semantic transmission strategy that encodes extracted semantics as base and enhanced semantics, progressively delivering them once the network becomes active. Numerical results indicate remarkable benefits of cross-modal semantic communications in terms of decision accuracy, model size, and transmission latency.