Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone’s Blind Source Separation

Abstract

Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) has a great potential of efficient application to a network of Internet of Things (IoT) of a high data rate. When the IoT network is amongst the underwater sensory devices known as the Internet of Underwater Things (IoUT), the electromagnetic wave can not play the role of baseband signal due to rapid fall off inside the water. Thus, Acoustic OFDM is a reliable replacement for conventional OFDM inside the water. A blind structure for MIMO acoustic OFDM using Independent Component Analysis (ICA) brings even further advantages in data rate and energy consumption by avoiding the required pilot and preamble data. This research work presents a blind MIMO Acoustic OFDM blind transceiver for IoUT based on Probabilistic Stone’s Blind Source Separation (PS-BSS). The proposed technique has multiple times lower complexity compared to the ICA-based technique while maintaining a comparable efficiency. As observed in the results carried out one hundred Monte Carlo runs of transmission random data bits, over a highly sparse channel that is the common case of an underwater environment the proposed PS-BSS-based technique dominates the ICA-based one, and as the sparseness of the channel decreases its efficiency is comparable to ICA-based technique. Thus in the case of a high sparse channel, the proposed technique is superior in both aspects of efficiency and complexity while over lower sparseness due to its comparative efficiency it can be hired as an optimum technique fulfilling a fair trade-off between efficiency and complexity.

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

“…For conventional algorithms, such as in [32], the researchers build an improved non-negative matrix factorization (NMF)-based BSS algorithm on a fast independent component analysis (FastICA) machine learning backbone to obtain a better signal-to-noise reduction and separation accuracy. Furthermore, a low-complexity method based on Probabilistic Stone's Blind Source Separation (PS-BSS) is proposed in [33] to be used in multi-input multi-output (MIMO) orthogonal frequency division modulation (OFDM) in the Internet of Underwater Things (IoUT). Artificial neural networks are also frequently used in similar situations.…”