# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 2 Related Works
### Channel State Prediction  
To resolve the inefficient and unstable communication caused by time-varying channel conditions, prediction methods for future channel states have been widely studied in recent years. Specifically, existing works can be classified into direct prediction and decomposition-based prediction methods, and direct prediction methods include linear and non-linear channel prediction methods. Linear prediction models have been used for channel prediction in wireless communication. The auto-regressive (AR) model was proposed in Reference [19] to predict the channel impulse response, which was expressed as a linear combination of current and past channel states. [PERSON] et al. [20] employed a channel prediction framework based on auto-regressive predictors to exploit both the spatial and temporal correlations among antennas. An improved adaptive Kalman estimator was proposed in Reference [21] for the adaptive fading channel. A recursive approximated structure with filter bank and discrete cosine transform was proposed in Reference [22] for channel prediction. For underwater channel state forecast, linear prediction models, such as statistical analysis [23] and exponential moving average (EMA) [24], were utilized, as well. In Reference [11], auto-regression was used every several symbols for the adaptive modulation of underwater communication. [PERSON] et al. [25] proposed an adaptive channel prediction scheme based on the exponential weighted recursive least square (EWRLS) algorithm, which used current and past estimated channel parameters in the delay-Doppler domain. Compared with linear prediction methods, non-linear channel prediction can achieve a smaller mean square error (MSE). A support vector machine (SVM) was employed in Reference [26] to predict channel state in airplane cabin scenarios. In Reference [27], an echo state network (ESN) was utilized for fast channel prediction in Ricean fading scenarios, which obtained smaller prediction error than previous designs. [PERSON] et al. [28] proposed novel channel prediction frameworks by using stochastic modeling, as well as data-driven learning of channel variability. A deep learning-based algorithm was proposed in Reference [29] to predict future channel state information (CSI) and received signal levels. Due to the complex fluctuation of underwater channel state with environmental noise, non-linear prediction methods can significantly improve accuracy [30]. [PERSON] et al. [30] introduced a channel prediction model based on nearest neighbor regression for underwater acoustic networks, where a fast search algorithm and a statistical storage compression method were used to optimize the time and space complexity of the prediction scheme. However, non-linear prediction methods usually show high computational complexity, while the computational capacity of underwater sensor nodes is limited. Due to the advantage of significant prediction accuracy, decomposition-based prediction methods have become promising solutions for channel prediction in the most recent years. [PERSON] et al. [31] introduced multi-resolution wavelet analysis to predict the received signal strength in the fast varying wireless environment. In Reference [5], the change process of underwater acoustic channel state was modeled as the sum of an environmental process affected by measurable environmental parameters and a Markov process explaining the contribution of unknown physical mechanisms. Based on the historical channel state sequence and recorded environmental parameters, a recursive algorithm was proposed to estimate the combination coefficient of the environmental parameters and the Markov process for channel prediction. The decomposition-based prediction methods show good performance [32] and the reason is as follows.