# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 1 Introduction  
Underwater acoustic (UWA) propagation is a complex probabilistic process dependent on many time-varying factors. For the general communication system, it is often desirable to establish a relationship between channel characteristics and the receiver decoding performance; however, the complex UWA environment often makes this a challenging task. Reliable prediction of the UWA communication performance is an enabling technology for many useful methods and systems, including adaptive networking and adaptive modulation strategies [1, 2], for both point-to-point communications and underwater sensor networks; please refer to [3, 4] for corresponding system characteristics and challenges. Previous works have focused largely on utilizing meteorological data [5] or signal statistics as input features [6]. These features are either observed directly through additional sensors, such as the meteorological data, or are engineered features estimated from signal statistics. The communication performance prediction is often cast as a classification problem by establishing ranges of performance. In the broader field of wireless sensor networks, decision trees and rule sets were introduced in [7] to predict a class of bit error rate (BER) to make routing decisions. Logistic regression was employed in [5] to estimate a range of packet success ratios based on environmental factors. A boosted regression tree was used in [2] to estimate the BER based on channel parameters such as the signal-to-noise ratio (SNR) and the delay spread, in order to apply an adaptive modulation scheme. In our earlier work [8], taking as inputs the engineered features derived from the full communication waveform, conventional regression methods and classification methods were applied to predict the communication performance. Specifically, for the orthogonal frequency-division multiplexing (OFDM) system, the derived engineered features includethe time-domain SNR, the pilot SNR that measures the SNR in the frequency domain in the presence of Doppler effect, and the channel root-mean-square delay spread. Despite providing reasonable prediction performance, deriving those features requires knowledge of the full OFDM waveform and additional receiver processing. To create a method that can be extended to the scenario where the full waveform is not available or costly to obtain, this work studies the performance prediction based on channel impulse responses (CIRs). Compared to the engineered features, the CIR can be easily obtained with an impulsive source or derived from a known broadband signal; a chirp is often used as part of the preamble in many UWA communications for channel sounding. In addition, the CIR contains more information than the engineered features. Convolutional neural networks (CNNs) have been heavily used in the field of computer vision and have been a breakthrough technology in the areas of image classification [9] and object detection [10]. Image processing methods have been extended to the domain of audio recognition by featurizing the audio signal into a 2D image through the use of mel-spectrograms and other related representations [11]. More recent work in the area of sound classification has explored the use of CNNs that only operate in one dimension [12]. Our work attempts to start with the CIR, a minimally processed input, and utilizes a CNN to interpret a meaningful performance prediction from that. The ability of the trained CNN as a feature extraction system is also explored by generating features and using them in conventional models. The proposed method can be extended to more challenging UWA channels, such as those with impulsive noise [13], by combining the CIR with impulsive noise for feature extraction. The rest of this paper is organized as follows. The data used for this work, including pre-processing used for conventional methods is presented in Section 2. The processing methods, both conventional and the proposed CNN method are introduced in Section 3.