On the Resilience of Underwater Semantic Wireless Communications

TL;DR

This paper evaluates the robustness of SAGE, a semantic communication framework combining semantic processing and GenAI, for underwater image transmission over acoustic links, demonstrating its resilience to channel errors.

Contribution

It introduces and assesses SAGE, a novel semantic communication system that leverages GenAI for efficient underwater image transmission, showing improved robustness over traditional methods.

Findings

SAGE can reconstruct meaningful images despite channel errors.

Semantic compression reduces data volume significantly.

SAGE demonstrates robustness in harsh underwater environments.

Abstract

Underwater wireless communications face significant challenges due to propagation constraints, limiting the effectiveness of traditional radio and optical technologies. Long-range acoustic communications support distances up to a few kilometers, but suffer from low bandwidth, high error ratios, and multipath interference. Semantic communications, which focus on transmitting extracted semantic features rather than raw data, present a promising solution by significantly reducing the volume of data transmitted over the wireless link. This paper evaluates the resilience of SAGE, a semantic-oriented communications framework that combines semantic processing with Generative Artificial Intelligence (GenAI) to compress and transmit image data as textual descriptions over acoustic links. To assess robustness, we use a custom-tailored simulator that introduces character errors observed in underwater acoustic channels. Evaluation results show that SAGE can successfully reconstruct meaningful image content even under varying error conditions, highlighting its potential for robust and efficient underwater wireless communication in harsh environments.