# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 2 Related Works
### Adaptive Data Transmission  
Adaptive transmission schemes have been studied in terrestrial wireless communications to improve communication performance. [PERSON] [35] investigated cross-layer scheduling and power control combined with adaptive modulation for wireless ad hoc networks. Reinforcement learning (RL) has also been used for adaptive transmission in terrestrial wireless communication. A transmission scheduling strategy based on deep Q-learning (DQN) was proposed in Reference [36] to maximize the system utility composed of throughput, buffer pressure, and power consumption for the cognitive Internet of Things. [PERSON] et al. [37] studied a throughput maximization problem based on deep Q-learning in a wireless communication system with energy harvesting and energy limited transmitter. For hybrid satellite-terrestrial relay networks, the performance of adaptive transmission was investigated in Reference [38] with a decode-and-forward relay. [PERSON] et al. [39] investigated adaptive margins for AMC in broadband satellite links during the actual rain event. To increase system throughput and improve transmission efficiency, an adaptive coding transmission (ACT) scheme was proposed in Reference [40] over the satellite-terrestrial channel based on the analog fountain code (AFC), achieving a seamless performance across all channel states. Underwater acoustic communication surfs from the following difficulties: large amplitude of channel variation, large propagation delay, narrow bandwidth, and low bit rate. Compared with terrestrial wireless sensor networks, the design of UASNs is more challenging. Adaptive transmission schemes based on rules have been investigated for underwater acoustic communication. [PERSON] et al. [1] proposed an AMC system to maximize the transmission rate with a given transmission power for underwater communication using orthogonal frequency-division multiplexing (OFDM). And the system utilized a finite number of transmission modes which were switched based on the effective signal-to-noise ratio (ESNR). Different modulation methods were used in Reference [10] for opportunistic cooperative transmission of underwater networks under various environmental conditions to achieve the best compromise between robustness and data rate. [PERSON] et al. [11] proposed a fixed threshold adaption algorithm and the required SNR of different modulation methods were presented to meet the target bit error rate (BER). An adaptive OFDMtransmission system was proposed in Reference [12] based on the SNR while maintaining a certain bit error rate (BER). Although these rule-based schemes are convenient to operate, fixed thresholds are not applicable for complex channel fluctuations and varying data transmission requirements, which will result in performance degradation. Some researchers investigated adaptive transmission based on learning algorithms for underwater acoustic communication. In Reference [41], [PERSON] et al. proposed an RL-based antijamming relay scheme, as well as a deep-RL-based relay scheme, for UWSNs to optimize the relay mobility and power allocation. [PERSON] et al. [13] designed two transmission schemes based on dynamic programming (DP) and a heuristic strategy to send a specified number of packets before a deadline while minimizing transmission attempts. To maximize the expected total amount of delivered data in finite time, energy management was investigated in Reference [14] for underwater acoustic nodes. And a stochastic dynamic programming algorithm was used to solve the optimal solution in energy allocation and a suboptimal algorithm was presented with reduced complexity. Reinforcement learning can be used to derive optimal actions for adaptive transmission in dynamic environments. A RL-based protocol was developed in Reference [42] for underwater acoustic communication to minimize a cost function which combined delay and energy consumption and ensure reliable transmission. [PERSON] et al.