Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

Abstract

Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods.

Submersible location prediction using SST turbulence and dynamic analysis

“…Deployment of equipment and establishment of search patterns using the position model: Based on the established position model, design the initial deployment points and search patterns for equipment to minimize the time to locate the missing submersible. Calculate the probability of finding the submersible as a function of time and cumulative search efforts [11] .…”

Performance Evaluation of OTFS-NOMA Scheme for High Mobility Users

“…The carrier frequency offset is represented as 𝑓 𝐷 = 𝑣 𝑓 𝑐 𝑐 ⁄ , where 𝑓 𝑐 is the carrier frequency, 𝑐 is the speed of the light and 𝑣 is the speed of the movement between the transceivers [15]. As seen in Table 2, it is considered that the maximum Doppler is set to 1410 Hz, and velocity is set to 380.7 km/h at a carrier frequency of 4 GHz.…”

A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature

“…The underwater acoustic communication receiver has a high bit error rate (BER) since it was constructed using the signal characteristics found in the Delay-Doppler feature [11]. The accuracy of signal features may be increased by using deep learning for the feature extraction of signals in the Delay-Doppler domain as the field develops and advances [12]. The Delay-Doppler features of signals are widely used in underwater acoustic communication.…”