Design of a Turbo-based Deep Semantic Autoencoder for Marine Internet of Things

TL;DR

Turbo-DSA tackles the challenge of efficient semantic transmission in marine IoT by integrating Transformer-based semantic encoding/decoding with a DL-based Turbo channel coder/decoder in an end-to-end framework. It enables joint source-channel coding at the semantic level and adapts to changing marine channels via a shared background knowledge base. Key contributions include a Transformer-based semantic front/back, a DL-based Turbo encoder/decoder, end-to-end training/testing algorithms, and extensive experiments showing superior BLEU and Sentence Similarity, especially under low $SNR$ and fading conditions. The results demonstrate faster convergence and robust semantic transmission performance, offering practical impact for reliable, efficient maritime communications.

Abstract

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

Figures (12)

• Figure 1: The system model of the proposed Turbo-DSA.
• Figure 2: The signal transmission process of the proposed Turbo-DSA.
• Figure 3: The BLEU scores versus SNR under different datasets.
• Figure 4: The SS versus SNR under different datasets.
• Figure 5: The BLEU scores versus SNR under different channels, using the maritime dataset 1.