## 1 Introduction
However, the proposed scheme, that utilizes a multi-stage directional acyclic graph (DAG)-net and a long-short term memory (LSTM), attains a low BER and large DoM. The main contributions of the proposed method are as follows,  
* For a large the DoM, we directly utilize many real dolphin whistles for the modulation. * For a small BER, we optimally classify the real dolphin whistles with large distances, and develop a trellis structured transmission algorithm using the information link matrix, without sacrificing the DoM. * For increasing the detection performance of the nonlinear characteristics of many transmitted whistles, we develop a DAG-net based machine learning detector. \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Author** & **Title** & **Year** \\ \hline [PERSON] et al. & Covert underwater acoustic communication using dolphin sounds [8] & 2013 \\ \hline [PERSON] et al. & Bionic communication by dolphin whistle with continuous-phase based on Minimum Shift Keying (MSK) modulation [9] & 2016 \\ \hline [PERSON] et al. & Biologically inspired covert underwater acoustic communication by mimicking dolphin whistles [10] & 2017 \\ \hline [PERSON] et al. & Bio-inspired steganography for secure underwater acoustic communication [11] & 2018 \\ \hline [PERSON] et al. & Multipath combining method for frequency shift keying underwater communications mimicking dolphin whistle [12] & 2018 \\ \hline [PERSON] et al. & Mimicking dolphin whistles with continuously varying carrier frequency modulation for covert communication [13] & 2019 \\ \hline [PERSON] et al. & Machine learning based dolphin whistle transceiver for bio-inspired underwater covert communication [14] & 2019 \\ \hline [PERSON] et al. & Time-frequency modulation based mimicking dolphin whistle for covert underwater acoustic communication [15] & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of bio-mimetic communication schemes. For increasing the BER performance, we combine the DAG-net and LSTM, i.e., D-LSTM, which utilizes the link information to demodulate the distorted whistles received by the UWA channels. * The performance of the proposed algorithm has been proved through computer simulations and practical ocean experiments. The paper is organized as follows. Section 2 describes the characteristics of the dolphin whistles and the whistle classification by groups. Section 3 proposes the modulation method that allocates bit information into whistles using the link information. Section 4 mentions the DAG-net based LSTM demodulator. In Section 5, the learning process of the proposed method is shown. Section 6 demonstrates the BER performance by using computer simulations and practical ocean experiments. Section 7 concludes the paper. In Figures 1 and 2, the similar whistle patterns are marked by rectangular and circular boxes. In Figure 1, the rectangular and circular boxes contain up-chirps with a different frequency variation and a large variation, respectively. In Figure 2, the rectangular and the circle boxes are marked for flat-downward scoops and down chirps, respectively. In practice, more whistle patterns than in the above examples can be found. If the whistles with similar patterns are classified as the same group and binary bits are allocated to the whistle group, we can transmit binary information with the same dolphin whistle patterns. Thus, the proposed method transmits one of the randomly selected whistle patterns in the group, which preserves a larger DoM and greater covertness than the conventional biomimetic UWA communication methods. When all whistles are classified as groups, the distance between groups needs to be kept as far as possible to attain the low BER. Thus, we classify the whistles to maximize the distance based on the whistle features.