# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Testing Framework
In this case, modulations QPSK or BPSK are considered (with higher occurrence of BPSK modulation exhibiting lower spectral efficiency) with medium packet length and medium gain to both preserve throughput and also cope with acoustic disturbance reduction due to the vulnerability of the considered species. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class Labels** & **Gain** & **Packet Length** & **Modulation** \\ \hline \multirow{3}{*}{1–3} & [10; 11; 12; 13; 14; 15] & [7; 8; 9; 10] & QPSK \\  & [10; 11; 12; 13; 14; 15] & [4; 6] & QPSK \\  & [10; 11; 12; 13; 14; 15] & [7; 8; 9; 10] & BPSK \\ \hline \multirow{3}{*}{4–6} & 5 & [4; 6] & BPSK \\  & [6; 7; 8; 9] & [4; 6] & QPSK \\  & [6; 7; 8; 9] & [4; 6] & BPSK \\ \hline \multirow{3}{*}{7–9} & 4 & 2 & BPSK \\  & 4 & [2; 3] & BPSK \\ \cline{1-1}  & 5 & 3 & BPSK \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration map table. * _Classes_ 7-9 (associated to rare species). In this case, a fixed BPSK modulation is considered (exhibiting lower spectral efficiency but better reliability) with low packet length and low gain to cope with acoustic disturbance minimization because of the fragility of the considered species. Observe that our approach is general enough to account for other possible alternative ways of performing integrated sensing based on the semantics (i.e., on the meaning) of the data collected. As an example, if the underwater nodes are equipped with hydrophones, based on the sounds registered, if certain frequency components are present (e.g., those related to the vocalisms of certain species), appropriate sensors are activated, e.g., a camera sensor, and a similar classification can take place with the corresponding assignment of communication parameters. This is because such frequencies could be a hint for the presence of a group of rare mammals which, thus, can be visually tracked. A more general layout of the whole framework is shown in Figure 3, summarizing the processing chain: the sensed data (an image in our prototype) enter the CNN for inference; the resulting label is passed to a configuration map that chooses the modulation type, packet length and transmit gain; these parameters drive the transmitter blocks, so that data traverse the underwater channel and can be recovered by the receiver. A software-defined radio (SDR) is a radio communication system where most key components that have traditionally been implemented in hardware are instead realized through software on a general-purpose computing platform. By using software to handle functions like modulation, demodulation, filtering, and signal processing, SDRs enable dynamic adaptation to new protocols, frequencies, and standards without requiring hardware changes. One of the most widely used tools for SDR development is _GNU Radio_[16]. GNU Radio is a free and open-source development toolkit that provides signal-processing blocks to implement SDRs. It allows users to create radio systems through a graphical user interface (GUI) or by writing Python (3.12 version) scripts. The flexibility of GNU Radio lies in its modular architecture, where users can drag, drop, and connect pre-built signal-processing blocks to design complex radio systems. GNU Radio is supported by a variety of hardware front-ends, such as universal software radio peripheral (USRP) devices and RTL-SDR dongles. Figure 2: GNU Radio flowgraph for CNN inference. Concerning the GNU Radio implementation used for the SDR, sketched in Figure 4, within the communication architecture, the payload comprising the classification results is read and passed through the Stream to Tagged Stream block. This converts the input data stream into defined packet sizes by creating boundaries, thereby enforcing the selected packet length. The CRC32 block generates a cyclic redundancy check (CRC) for the payload to ensure data integrity. A header section is generated for the payload.