# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction
### Conventional OFDM Channel-Estimation Methods  
Channel estimation in OFDM systems plays a major role in helping the receiver to estimate the channel state information, which is used at the receiver to compensate for channel distortions. In general, traditional pilot-assisted algorithms for channel estimation are adopted, in which pilot symbols are transmitted with the data in order to estimate the characteristics of the channel [8]. Among them, least square (LS) represents one of the most-used techniques because it is simple. However, while the LS estimation represents a simple method not requiring a priori information about channel or noise characteristics, it is very sensitive to channel noise; thus, its reliability is less in dynamic underwater conditions. For instance, the authors in [9] proposed an LS method for sparse channels estimation. Channel coefficients for OFDM are estimated by applying this method and the MMSE method. Following, the authors in [10], the conventional LS channel-estimation algorithm is analyzed. This approach takes advantage of the scarcity in the channel response, allowing more precise estimates that reduce the impact of fading and the noise in UWA environments. Recently, ref. [11] have introduced a sparse channel-estimation scheme of optimized least squares which considerably improves the efficiency of communication. In addition, the authors in [12] discuss challenges such as propagation of multiple paths and noise, offering solutions such as improving the pilot design. Their work highlights the need for current research to improve the reliability and performance of submarine channels. Recent advances in joint and noise channel-estimation techniques for submarine acoustic systems have demonstrated significant improvements. For instance, refs. [13, 14] explore methods that effectively estimate impulsive noise together with the characteristics of the channel, highlighting the importance of precise noise modelling in real-world applications. These findings emphasize the need for solid estimation techniques to improve the reliability of communication in challenging underwater environments. The work in [15] focuses on long range UWA channel estimation. Their study deals with the challenges posed by the unique properties of UWA signals, providing a robust picture for the esteem of the channel that improves the reliability and efficiency of the communication. Furthermore, ref. [16] offers a technique based on pilot-based channel estimation, which shows promising efficiency in variable channel conditions. In comparison, recent progress in the estimate techniques of the UWA channel have led to innovative algorithms aimed at improving the accuracy of the signal processing. A remarkable contribution is the algorithm of recursive least square (RLS) proposed by [17], which improves the efficiency. This algorithm takes advantage of blocking responses to optimize performance in demanding submarine environments. Together with the techniques of the existing literature, the RLS algorithm represents a significant step forward in the facilitation of underwater communication systems. The pilot-based adaptable channel estimated significantly advanced underwater spatial modulation technologies, increasing the reliability of communication in challenging aquatic environments. These advances, as observed by [18], address critical challenges, such as impacts by multiple and variable channels in time. Future applications are promising, indicating possible improvements in underwater communication systems. The algorithm of the LS plays a crucial role in the estimation of the selective frequency fading channel. Moreover, the RLS algorithm in conjunction with the variable forgetting factor is developed in [19] for estimating a frequency-selective fading channel. The variable forgetting factor successfully makes up for the loss in the tracking capability caused by the insufficient polynomial order with almost less computational complexity.