Underwater Communication Signal Recognition Using Sequence Convolutional Network

Abstract

Automatic modulation recognition (AMR) is one of the essential parts in the intelligent communication system. In the underwater acoustic communication, it is a challenging work that promptly and easily recognizes the signal modulation schemes by conventional methods. The deep neural network method is a good solution to the problem, which creates a better recognition effect. The packets of data that are fed to the familiar neural network is constant. However, the packets of signal data on the communication course consistently change, which seriously reflects on the signal recognition veracity. A novel deep learning network with the sequence convolutional network in this paper is proposed, which is composed of one-dimensional sequence convolution of residual network modules and the variable convolution kernel range. By extracting the time-domain signal characteristics, the affection of various signal packets can be mitigated. In experiments, the employed network not only has more concentrated on the modulation recognition veracity, but also owns a lower parameter quantity and a shorter training time, which indicates ideal recognition results in the underwater communication environment. Moreover, it is more valuable to the real underwater communication system.

Modulation Recognition Method for Underwater Acoustic Communication Signals Based on Passive Time Reversal-Autoencoder with the Synchronous Signals

“…Kong et al [16] used IQ symbols to train a residual network. Wang et al [17] proposed a sequence convolutional network to achieve modulation classification based on signals' temporal characteristics. Liu et al [18] utilized principal component analysis technology to compress the original time-domain signals and then designed a deep heterogeneous network for modulation recognition.…”

Multihydrophone Fusion Network for Modulation Recognition

“…Different DL networks are selected for different shallow features. Common DL networks include the long short-term memory network [10], convolutional neural network (CNN) [11], capsule network [3], generative adversarial network [8], autoencoder network [12], and residual network [13]. With the combination of different DL networks and shallow features, DL-based methods have made considerable progress in the field of modulation recognition.…”

Real-time spectrum sensing for inference and control

“…[59] develops a blind signal detector for underwater acoustic signals, essentially distinguishing the signals from noise, and developing a transfer model reducing the reliance on simulated data for online testing. [60][61][62] develop a modulation classifier for underwater acoustic channels using DL techniques and validate in data from real experimental scenarios. While the results of many of these works are promising and are shown to perform with data from real underwater scenarios, they are limited in identification complexity.…”