Iterative Learning for Reliable Link Adaptation in the Internet of Underwater Things

IEEE Access

Junghun Byun¹  Kyungseop Shin²  Juyeop Kim³  Yong-Ho Cho⁴  Taeho Im⁵  Hak-Lim Ko⁵  Ohyun Jo⁶ 

[1] Department of Computer Science, Chungbuk National University, Cheongju, South Korea;Department of Computer Science, Sangmyung University, Seoul, South Korea;Department of Electronics Engineering, Sookmyung Women&x2019;Department of Electronics, Information and Communication Engineering, Mokpo National University, Mokpo, South Korea;Department of Information and Communications Engineering, Hoseo University, Asan, South Korea;s University, Seoul, South Korea;

关键词: Link adaptation;    adaptive modulation and coding;    underwater wireless communications;    machine learning;    Internet of Underwater Things;   

DOI  :  10.1109/ACCESS.2021.3058981

来源: DOAJ

【 摘 要 】

Given the ever-increasing interest in the Internet of Underwater Things (IoUT), various studies are ongoing to solve some of the practical problems affecting the development of underwater wireless communication. The main problems are related to the use of acoustic waves in a water medium, in which extremely high propagation loss and drastic channel fluctuation are common. On the basis of hands-on experience and measurements made in real underwater environments, the conventional Adaptive Modulation and Coding (AMC), which uses the high correlation between SNR (Signal to Noise Ratio) and BER (Bit Error Rate), might not be affordable in underwater environments because the normal correlation between SNR and BER almost disappears altogether. This work therefore collectively takes into account multiple quality factors of communication at the same time by creating, analysing and validating the machine learning model to predict the most adequate communication parameters to solve the problem. The dataset of underwater wireless communication used in the learning models was obtained from measurements made in a real underwater environment near the Gulf of Incheon, South Korea, using a practical testbed designed and implemented by the authors. The estimated network throughput based on the communications parameters predicted using the machine learning models was enhanced by up to 25% compared with the conventional handcraft method.

【 授权许可】

Unknown