# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
### Related Work  
Several studies have explored link prediction techniques. Specifically, [PERSON] et al. [3] proposed a wireless backhaul network formed by shipborne adaptive backhaul devices that connected to terrestrial base stations. It also introduced a controller for static nodes within the base station network, which analyzed the connections based on signal strength, background noise, and link quality. In [4], a software-defined maritime communication framework was proposed that incorporated an improved deep Q-learning algorithm (softmax deep Q-network, S-DQN). This framework used a Markov Decision Process (MDP) to optimize the network resource scheduling, and through self-learning from large datasets, it efficiently selected the optimal strategies. The work in [5] tackled the challenge of predicting the network quality in mmWave and sub-terahertz systems for 5G and 6G networks, which experience rapid fluctuations in their signal quality due to obstructions and changes in a device's orientation. This study proposed the use of a Liquid Time-Constant (LTC) network for more accurate multi-directional quality predictions, demonstrating a superior performance. Furthermore, [PERSON] et al. [6] addressed the challenge of unreliable wireless links in smart grids due to interference and stochastic behavior. They proposed a method for predicting the lower boundary of the link quality's confidence interval. Using wavelet denoising and decomposing the signal, this approach ensures reliable transmissions by evaluating the worst-case link quality. [PERSON] et al. [7] solved the problem of selecting reliable communication links in wireless sensor networks by proposing an improved link quality estimator (LQE-IWELM). It utilized [PERSON]'s correlation to evaluate the link quality parameters and optimize them with a Particle Swarm Optimization (PSO) algorithm, thereby enhancing the network stability and link selection. [PERSON] et al. [8] proposed a three-stage joint channel decomposition and prediction framework with low latency and low pilot overhead for acquiring channel state information in a Reconfigurable Intelligent Surface (RIS)-assisted system. Firstly, by utilizing the quasi-static characteristics of the base station (BS)-RIS channel and the quickly time-varying characteristics of the RIS-User Equipment (UE) channel, the first stage accurately estimated the channel using full-duplex technology, effectively solving the scale ambiguity problem. In the subsequent two stages, a novel Sparse Connected Long Short-Term Memory Network (SCLSTM) was adopted. And an algorithm was developed which was capable of decomposing the channel and capturing its temporal correlation to predict the CSI more accurately, robustly, and effectively. [PERSON] and [PERSON] [9] proposed a novel link connnectivity estimation method based on IoT and WSN devices. The purpose was to estimate the link quality in noisy environments with low complexity. In addition, the effect of the SNR on link quality was investigated. The symbol error rates were extracted from simulations, and a relationship model was established through curve fitting. In the analysis of link communication connectivity, deep learning has emerged as a promising solution due to its ability to extract implicit features from large amounts of historical data. Recurrent Neural Networks (RNNs) and their improved version, LSTM,have demonstrated exceptional performance in handling time-series data [10]. Particularly in communication systems, they have shown remarkable applications in tasks such as channel estimation, channel state prediction, and modeling dynamic changes in the network topology, highlighting their broad application potential. Currently, numerous other deep learning methods have also been applied in this field. The scholars in [11] explored incorporating and made an attempt to incorporate machine learning (ML) into maritime communications, addressing challenges such as the channel selection, coding, synchronization, and positioning. [PERSON] et al.