# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction
### Existing Work in DNN Based OFDM Receiver
Unlike existing receivers which need a neural network and several other processing parts, the proposed end-to-end receiver only uses a single neural network to implement the entire signal processing. In [30], the authors present an AI-aided online adaptive OFDM receiver design, addressing the performance gap between simulation and experimental tests. The SwitchNet receiver's flexible architecture allows for online training of select parameters, adapting to real-world channel conditions. Validation through real-time video transmission tests confirms the receiver's robustness and effectiveness in enhancing communication system performance. In addition, the researchers in [31] delve into the use of LSTM networks for UWA OFDM communication. Their study proposes an intelligent receiver design that integrates traditional signal-processing modules with an LSTM-based channel estimator. The study's simulation results validate the improved performance of the LSTM-based system, suggesting a promising direction for future research in intelligent receiver designs for underwater communications. Addressing the scarcity of UWA channel samples, the ref. [32] introduces a DNN-based UWA OFDM channel-estimation scheme. It employs a model to generate enhanced channel samples, overcoming the limitations of sample collection in UWA environments. The research also proposes a novel channel-estimation architecture combining a U-Net structure for feature extraction and a channel attention denoising module. Validation in various underwater environments shows significant improvements in mean square error and BER compared to traditional and other deep learning-based models. This study underscores the importance of data augmentation in enhancing the performance of deep-learning models in UWA communications. In [33], the authors propose a deep learning-based UWA OFDM communication system, where the receiver, designed as a DNN, recovers transmitted symbols. The DNN receiver undergoes training with labeled data and is validated using data from an acoustic propagation model. However, the study highlights the need for further research to enhance the model's generalizability to different underwater conditions. The researchers shift focus to the design and simulation of an intelligent receiver for UWA OFDM communication using LSTM networks. In addition, the ref. [34] proposes a deep learning-based receiver for UWA OFDM communications, designed to recover transmitted symbols without explicit channel estimation and equalization. The DNN-based receiver, trained with labelled data from an acoustic propagation model, demonstrates significant performance improvements. However, the study acknowledges challenges in generalizing the model to diverse underwater environments, suggesting further research to address these limitations. However, despite these several developments, the deep learning-based channel-estimation techniques are still affected by a number of issues, such as low generalization across different environments, long computational training, and high complexity in parameter tuning. These systems are far from being feasible for UWA communication systems' real-time applications due to huge training datasets and high memory requirements. One of the promising solutions to these challenges is TL, which enjoys some key advantages compared to traditional deep-learning methods. TL allows models to adapt to new environments with minimal retraining, reducing training time and the amount of required data. Moreover, TL helps solve the generalization problem, as it enables the network to transfer knowledge from one environment to another, thus making it more robust to environmental changes as found by studies in the wireless communication domain in [35]. This approach has the potential for great improvements in OFDM-based communication systems that might adapt to new, real-world UWA environments without exhaustive retraining.