# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction
### Existing Work in DNN Based OFDM Receiver  
Given the limitations in conventional OFDM channel-estimation methods, a rising interest in deep learning techniques has been focused on channel estimation. The recent works show that the DNN based methods of channel estimation may learn channel characteristics directly from the received signals with a performance superior to conventional methods. These methods bypass the need for traditional pilot-assisted techniques, thereby simplifying the overall system design. Several studies have proposed DNN based channel-estimation solutions, showing significant improvements over traditional methods. For instance, the authors in [22] suggested replacing the channel equalization and demodulation blocks of traditional OFDM receivers using a five-layer FC-DNN. The efficiency of their model was shown to outperform traditional receivers, however, FC-DNNs have significant drawbacks, particularly in UWA environments. These networks are highly susceptible to small perturbations and distortions, which makes them unsuitable for the unpredictable and dynamic nature of underwater channels. Moreover, the drawback of FC-DNNs is their need for abundant data to train, since parameters increase exponentially with the number of neurons or layers, which is infeasible for UWA hardware with limited resources. Despite their advantages, FC-NN based methods are not free from challenges either. Adapting to the real time dynamic underwater conditions is a challenge even now, while the training data requirement is huge. Training models on static ray tracing data generated by tools such as BELLHOP [22] fails to represent dynamic real UWA environments and further constrains the generalization of such models. In this respect, the use of convolutional neural networks (CNNs) has been explored as an efficient alternative in UWA OFDM communication systems to overcome such limitations. Unlike FC-NNs, CNNs have better feature-extraction capabilities and are more robust to small distortions; therefore, they are well suited to handle multipath propagation and ocean noise effects. For instance, the proposed DNN in [23] is trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models. Building on the ground-work established in channel estimation using DNN models, the authors in [24] put forth a framework using a DenseNet model that improves the robustness of channel estimation under different watermark channels. The dense connectivity strategy of DenseNet enables the model to effectively perform channel estimation. In addition, a CNN-based equalizer was proposed in [25] to compensate for signal distortion and showed promise in handling ISI. However, this was a relatively simple model. Based on these developments, ref. [26] proposed a novel CNN-based OFDM receiver utilizing skip connections and a multi-layer perceptron for signal recovery and demodulation, showing significant improvements in BER performance in challenging underwater environments. The UACC GAN model considerably improves UWA communication by improving efficiency and reliability thanks to its advanced channel-simulation capabilities. These innovations allow better performance in dynamic oceanic environments, thus opening the way to new applications in ocean engineering [27]. The potential of the model lies in its complete analysis of communication channels. In [28], the authors explore an innovative OFDM receiver design based on DNN. They proposed a regression-based DNN using a long short-term memory layer to directly recover transmitted bits. The validation is carried out through simulations, demonstrating the effectiveness of the proposed design. Building on this foundation, ref. [29] proposes a DNN-based OFDM receiver for UWA communication.