## 1 Introduction
Another option is to build simple networks for the module optimization. [PERSON] et al. proposed an NN-based generalized frequency division multiplex with index modulation (GFDM-IM) detector to detect the symbols after a coarse detector [32]. With perfect CSI in the receiver, the simulation results showed lower BERs of this network than the ZF detector. [PERSON] et al. have built a CNN for index modulated OFDM (IM-OFDM) detection, whose performance approximates the maximum likelihood (ML) detector [33]. A further option for NN is the extreme learning machine (ELM)-based receiver. This kind of receiver integrates the channel estimator and equalizer with a single layer NN, which is trained online for each time [34; 35]. In [PERSON]'s work, with enough block pilots for training, a long frame with a large quantity of OFDM symbols was simulated [35], showing better performance than MMSE equalizer and NN-based detector proposed by [PERSON] et al. [22]. Since the UWA channels were time-variant, the ELM detector was unable to show good performance, because the transmitted frames had to be kept short to reduce the influence of the time-variant channel. [PERSON] et al. [36] proposed a transfer strategy for the DNN-based OFDM receiver and tested it with the WATERMARK dataset and real experimental data. This study focused on the network retraining, and did not discuss the design of the applied NN structure. [PERSON] et al. [37] focused on solving the channel sample augmentation problem for the NN-based channel estimator. Both [36; 37] proposed an innovative strategy to solve the application problem of DNN-based receievers. Despite the good simulation results produced by the above structures, problems still exist when the system is implemented in UWA channels. * Firstly, the real dataset is difficult to obtain because the UAC links are usually one-way with no feedback. Hence, the uncertain time-variant channel states do not allow the system to obtain the samples in a short period. * Secondly, it is not realistic for the NNs to be retrained in a high rate link because the computation loading is still heavy for real-time applications. Consequently, the data-driven works mentioned above barely discuss the performance of the system with real experiments. In this paper, an attempt at the design and derivation of an NN-based receiver is made for the UWA OFDM system. A simple NN is proposed to integrate the equalization and symbol detection, containing only one fully-connected layer. Firstly, taking both channel frequency response and received symbol as input, the network learns a robust structure to output the symbol directly. To minimize the complexity of the network, the channel and received symbol are divided into blocks of the same size, matched with small-size networks. For attribution to the simple structure, the hyperparameters (mainly the hidden layer size and input dimension) are inferred according to the delay and coherence bandwidth of the channel. Thus, the channel-driven networks are constructed. After being trained with mixed channels and noise samples, the networks show robustness in both simulations and sea trials, performing better than the ZF and MMSE equalizers as well as the ELM-based detector in [35]. The contributions of this paper are listed as follows. * We propose a low-complexity NN-based symbol detector for the UWA OFDM system. The network takes the segmented channel response and symbol block as input and integrates the equalization and detection processes. The small input dimension also reduces the requirement for the hidden neurons. The proposed detector shows lower computational complexity than the MMSE and ELM-based detectors. * The NN-based detector is trained offline with a channel dataset containing simulated and real channels. Then the detector can be applied completely independently online with fixed hyperparameters, improving the efficiency of the online receiver.