Xiaoyan Kuai

Due to the limited spectral resource and frequency selectivity of underwater acoustic channel, beamforming is an efficient method to improve the performance gains and realize the large-scale deployment of underwater communication network. In the paper, the multiuser OFDM-based MISO underwater acoustic communication system is considered and a convolutional neural network (CNN) based beamforming framework, i.e., the BFNet, is proposed. The proposed CNN framework takes the channel matrix in the frequency domain as input, and the centralized transmitter generates the beamforming vector for each user to solve the problem of sum-rate maximization. Simulation results show that the BFNet framework achieves better or comparable spectral efficiency, lower computation complexity compared with conventional benchmarks.

Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.