Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

Automated summary

The paper proposes a method to control the energy efficiency of data transmission in underwater acoustic sensor networks by adjusting modulation methods, coding rates, and transmission power based on the dynamic nature of the communication channel. They introduce a double-scale adaptive transmission mechanism that predicts channel states and develops an energy-efficient transmission algorithm to optimize modulation and coding in the long term. A quantitative model is constructed to analyze the relationship between data transmission and buffer thresholds under different channel states or data arrival rates.