## 6 Conclusions  
In this paper we presented two statistical models to characterize the underwater acoustic channel in network simulators, which well-matched the results observed during sea trials. Specifically, we were able to develop two precise channel models for underwater communications, starting from the analysis of real field experiment data retrieved from ASUNA. The models are based on two- and three-state Markov chains and have two main advantages: first, they guarantee a realistic channel modelling with respect to the results observed during sea trials; second, they are not particularly computationally demanding. Indeed, while for the two-state HMM the PER can be simply computed with a closed formula, for the three-state HMM the PER can be computed iteratively, starting from the last PER computed during the ongoing simulation. The two models revealed themselves to be adaptable to multiple configurations and flexible. Furthermore, they have both been extensively tested after having been implemented in the DESERT simulator, and they have been compared with the existing legacy channel model already available in the simulator. The performance obtained with the two models were solid and proved their reliability,  
Figure 11: “Instantaneous” throughput values yielded by UWPhysical module from node 1 to node 3 (a) and from node 4 to node 2 (b).  
Figure 12: “Instantaneous” throughput values yielded by UWHMMPhysical module from node 1 to node 3 (a) and from node 4 to node 2 (b).  
Figure 13: “Instantaneous” throughput values yielded by UWHMMPhysicalExtended module from node 1 to node 3 (a) and from node 4 to node 2 (b).  
with the three-state HMM slightly outperforming the two-state HMM, at the cost of a small increase in complexity. Possible future work may consist of investigating models with an increased number of states (i.e., more than 3), and studing the tradeoff given by the increased computational requirements and the fidelity of the results. Another aspect that deserves a specific investigation is how this channel model can be applied to mobile networks, e.g., by extending the number of states or including a penalty factor due to distance, speed and acceleration. This aspect is not trivial, as the increase of a node speed does not cause only a strong Doppler effect, but also a strong acoustic noise caused by propellers and engine [24].  
Conceptualization, F.C. and N.T.; methodology, N.T. and F.C.; data curation, N.T.; investigation, F.C.; formal analysis, N.T. and F.C.; software, N.T.; validation, F.C.; writing-original draft preparation, F.C.; supervision, M.Z.; resources, M.Z.; visualization, N.T. and F.C.; project administration, F.C. and M.Z.; funding acquisition, F.C. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by the European Union--FSE REACT EU, PON Research and Innovation 2014-2020 (DM 1062/2021).  
[[https://desert-underwater.dei.unipd.it/](https://desert-underwater.dei.unipd.it/)]([https://desert-underwater.dei.unipd.it/](https://desert-underwater.dei.unipd.it/)), [[https://sites.google.com/marsci.haifa.acil/asuna/](https://sites.google.com/marsci.haifa.acil/asuna/)]([https://sites.google.com/marsci.haifa.acil/asuna/](https://sites.google.com/marsci.haifa.acil/asuna/)), accessed on 21 June 2022.  
The authors declare no conflict of interest.