# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
###### Abstract  
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. s 202122520212220212202122021220212202122021220212220212 fluctuation in underwater communication channels. Figure 1 presents an example of the channel states in an underwater communication experiment [9]. Obviously, the fluctuation of the received signal-to-noise rate (SNR) is roughly consistent with that of the wind speed. Based on the above observation, we propose to take advantage of the historical channel state series and analyze the fluctuation characteristics for channel state prediction, so that an optimized configuration scheme can be derived and the energy efficiency of data transmission would be enhanced accordingly. However, it is quite challenging to schedule the optimal configuration scheme dynamically due to the following reasons. First of all, the prediction of underwater channels is difficult as they are impacted by multiple factors, such as underwater geology, salinity and depth, environmental factors (ocean current, surface wind, sea wave, solar radiation, etc. ), human-being activities, and fish behaviors, and it is hard to model the fluctuation by just using long time-scale historical channel state data or just using short time-scale cycle information. Secondly, to improve the overall performance of underwater sensor networks, the transmission rate and energy cost should be jointly optimized based on the predicted channel states. Thirdly, considering the limited computational and storage capability of underwater sensor nodes, how to balance the network quality and reducing complexity is essential. For example, even though the quality of network service, such as network throughput, can be improved with a fine-grained transmission scheme, the frequent change of transmission modes might cause extra computational complexity and energy cost. In the literature, adaptive transmission according to channel state has become a hot topic. The rule-based adaptive modulation and coding (AMC) methods usually utilize fixed thresholds for transmission mode determination [1; 10; 11; 12]. These methods are easy for implementation and could be used in the resource-constrained networks, while the threshold selection should be carefully designed. Learning strategies [13; 14; 15; 16] have been proposed to provide intelligent transmission decisions to characterize the dynamic communication channel, and the Markov chain is widely adopted.