Optimizing Adaptive Communications in Underwater Acoustic Networks

We consider an Underwater Acoustic Network (UAN) where each node is equipped with a suite of signals and so there is the flexibility to aim for different bit rates at each transmission slot. A Cross-Entropy (CE) centralized algorithm is explored to optimize the combination of modulation scheme and transmission power level in the presence of unreliable channels. Optimization metrics such as throughput, energy per bit, latency and their combination are considered. The motivation for this research stems from the fact that surveillance networks using battery-powered Autonomous Underwater Vehicles (AUVs) need to be able to promptly deliver critical data while prolonging their lifetime and reducing the footprint of their transmissions. The proposed strategy has been validated by post-processing thousands of acoustic signals recorded during the Littoral Acoustic Communications Experiment 2017 (LACE17) sea trial in the Gulf of La Spezia, Italy. Our analysis shows the trade-off between the bit rate and the transmission power given the selected optimization metrics. The solution computed when combining all the considered metrics makes possible to improve up to three times the throughput performance and up to one order of magnitude the energy consumption with respect to considering single other optimization metrics.