Assessment of Wide-Sense Stationarity of an Underwater Acoustic Channel Based on a Pseudo-Random Binary Sequence Probe Signal

• Main Author: Iwona Kochanska
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Applied Sciences
• Subjects: underwater acoustic communications; wide-sense stationary; pseudo-random binary sequence
• Online Access: https://www.mdpi.com/2076-3417/10/4/1221

The performances of Underwater Acoustic Communication (UAC) systems are strongly related to the specific propagation conditions of the underwater channel. Designing the physical layer of a reliable data transmission system requires a knowledge of channel characteristics in terms of the specific parameters of the stochastic model. The Wide-Sense Stationary Uncorrelated Scattering (WSSUS) assumption simplifies the stochastic description of the channel, and thus the estimation of its transmission parameters. However, shallow underwater channels may not meet the WSSUS assumption. This paper proposes a method for testing the Wide-Sense Stationary (WSS) part of the WSSUS feature of a UAC channel on the basis of the complex envelope of a received probe Pseudo-Random Binary Sequence (PRBS) signal. Two correlation coefficients are calculated that can be interpreted, together, as a measure that determines whether the channel is WSS or not. A similar wide-sense stationarity assessment can be performed on the basis of the Time-Varying Impulse Response (TVIR) of a UAC channel. However, the method proposed in this paper requires fewer computational operations in the receiver of a UAC system. PRBS signal transmission tests were conducted in the UAC channel simulator and in real conditions during an inland water experiment. The correlation coefficient values obtained using the method based on the envelope of a probe signal and the method of analysing the TVIR estimates are compared. The results are similar, and thus, it is possible to assess if the UAC channel can be modelled as a WSS stochastic process without the need for TVIR estimation.