# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 1 Introduction  
A swarm of autonomous underwater vehicles (AUVs) can be the key for a better exploration of the underwater environment. Many underwater applications can benefit from the usage of several underwater vehicles working together. Possible applications include seabed mapping, plume tracking [1], studies on fish migration [2], sea pollution monitoring, corrosion prevention in offshore installation of wind farms, undersea surveillance [3], among other civilian and military applications [4]. The main motivation for using many AUVs is that a single AUV might not be able to perform the same task as a swarm. As an example, the usage of a single underwater vehicle poses critical constraints for tracking plumes [1]. The main problem is the aliasing of spatial or temporal samples. For the cases of sensing small-scale features in the water, the AUV needs to perform high spatial sampling, which limits the survey coverage area (not enough space resolution). For the cases of dynamic features, such as hydrothermal vent plumes, the plume moves before the AUV survey is completed (lack of time resolution) [1]. Therefore, the usage of a set of coordinated AUVs can address all these aforementioned issues. Another benefit of a swarm of AUVs is that it can improve the localization accuracy for navigation purposes. In addition to that, a swarm enables the coverage of a wider area, and data acquisition with a better quality (without aliasing problems). The benefits go beyond, and include a better system reliability [4]. A swarm of AUVs is composed of several autonomous vehicles that act in a coordinated manner. They are designed to work together for accomplishing a pre-established common mission. In order to enable the collaboration among the distinct AUVs belonging to the same network, a reliable communication is mandatory [5]. The communication system is composed of several stages, including the medium access control and the physical layer. A possible way of improving the overall system performance is through the adaptation of some physical layer parameters. The adaptation of such parameters can be an efficient way of maintaining the quality-of-service in the highly time-variant acoustic channel. The majority of the adaptive approaches available in the literature requires some knowledge of the channel, which are obtained after an initial information exchange between transmitter and receiver, i.e., they require a feedback link. As an example, in [6] their objective is to maximize the throughput, with the constraint of maintaining an average bit error rate (BER). In this case, the adaptive parameters of the OFDM system are the modulation and/or the power level of each subcarrier. For performing these adaptation, this approach utilizes a channel prediction of one travel time ahead. The work from [7] aims to maximize the transmission rate for a given transmission power. For achieving this objective, they propose a new performance metric for selecting the most convenient modulation and coding scheme. This pair of adaptive parameters belongs to a previously defined set of transmission modes. Another reference [8] proposes the usage of a sparse principal component analysis (SPCA) for the selection of modulation and coding parameters. Some recent works propose the usage of reinforcement learning [9; 10] and of machine learning (ML) [11] algorithms for adapting the modulation and coding parameters. The works from [12; 13] utilize a multi-user game theory approach for adapting the power level of each user in an OFDM system. All these aforementioned works relies on some kind of channel state information (CSI). Although, for some underwater applications, this feedback link might not be available. A possibility is to utilize high-level environmental information for improving the system performance. The reference [14] proposes a blind adaptive modulation technique, which is based on machine learning. This approach depends on the accuracy of the considered propagation model.