Design of a turbo-based deep semantic autoencoder for marine Internet of Things

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Xiaoling Han , Bin Lin , Nan Wu , Ping Wang , Zhenyu Na , Miyuan Zhang
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引用次数: 0

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.
为海洋物联网设计基于涡轮的深度语义自动编码器
随着全球海洋经济的快速增长和海上活动的蓬勃开展,海洋物联网(IoT)正获得前所未有的发展势头。然而,目前的海洋设备在数据传输效率和语义理解方面存在不足。为解决这些问题,本文提出了一种新型端到端(E2E)编码方案,即基于 Turbo 的深度语义自动编码器(Turbo-DSA)。Turbo-DSA 通过发射器和接收器的 E2E 设计实现了语义层面的源信道联合编码,同时学会适应环境变化。语义编码器和解码器由转换器技术组成,可有效地将信息转换成语义向量。在神经网络训练过程中,这些向量会根据信道特性和背景知识库进行动态调整。Turbo 结构进一步增强了语义向量。具体来说,信道编码器利用 Turbo 结构分离语义向量,确保意义的精确传递,而信道解码器则利用 Turbo 迭代解码优化语义向量的表示。目标函数、语义提取和整个训练过程的设计确保了变换器与 Turbo 结构的深度融合。与传统的 Turbo 编码技术相比,Turbo-DSA 的收敛速度更快,这要归功于它对语义向量的高效处理。仿真结果表明,Turbo-DSA 在关键性能指标上超越了现有基准,如双语评估底层研究得分和句子相似度。这一点在低信噪比条件下尤为明显,它显示出卓越的文本语义传输效率和对多变海洋信道环境的适应性。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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