# BigGRU-Based Adaptive Receiver for Indoor DCO-OFDM Visible Light Communication
## 1 Introduction
Deep Neural Networks (DNNs) manifest an inherent capability to apprehend and analyze channel attributes, marking them as plausible solutions for CE. Notably, DNNs have been employed to supplant traditional channel estimation methodologies, reducing reliance on pilot symbols and preserving spectrum resources [10]. Similar approaches, encompassing DNNs [13; 14], Convolutional Neural Networks (CNNs) [15], Recurrent Neural Networks (RNNs) [16; 17], and diverse neural networks (NNs), have demonstrated efficacy as nonlinear equalizers in diverse scenarios to effectively counteract nonlinear impairments. However, extant studies have predominantly necessitated converting input for NNs from complex vectors, after Discrete Fourier Transform (DFT), or fast Fourier transform (_FFT_), into real vectors. Moreover, the symbol classifier, integral to preprocessing labels, necessitates preemptive transformation of M-ary quadrature amplitude modulation (M-QAM) constellation points into fixed labels. The present research introduces an innovative receiver scheme centered on neural network-based waveform equalization. This scheme exhibits the capacity to adeptly acquire the requisite skills for processing received sample signals. It achieves this by adeptly harnessing the channel state information, thereby markedly enhancing the reliability and efficacy of signal transmission. It is worth noting that this approach significantly departs from all preceding receiver methodologies. In this novel paradigm, the neural network undertakes data processing prior to engaging in the _FFT_ procedure. A pivotal aspect of this paradigm is that the input and anticipated output configurations of the neural network retain coherence with those of conventional receivers. This congruence in input-output structure translates to a consequential advantage, allowing for the direct integration of this scheme into orthogonal frequency division multiplexing (OFDM) systems, devoid of supplementary superfluous processing steps. Consequently, the scope of this investigation is distinctly focused on the channel model and the intricate channel state information germane to the visible light communication (VLC) link and explores how to leverage this information to optimize the performance of the DCO-OFDM system. In this paper, we introduce two existing reception methods and propose a DL-based direct reception method superior to these schemes for application in the DCO-OFDM-VLC system. (1) Traditional method: pilot-based LS and MMSE CE method. (2) DL-based typical receive: nonlinear processing power of DL is used to replace the traditional CE and equalization signal processing after _FFT_. (3) DL-based direct receive: this method uses DL to process the received signal, can directly learn the interference of nonlinear devices and channels on the signal, and reduces the complex data processing required in existing scenarios, and thus is different from (2). To obtain a better performance receiver, we compare the performances of two DL receivers: BiLSTM and BiGRU. The performance was evaluated using the bit error rate (BER) [18]. The feasibility of the direct receiver in a commercial LED-based single-light 4 QAM DCO-OFDM-VLC system with a transmission distance of 3 m was verified by changing the transmission data rate and receiving the optical power of the experimental setup. The rest of this article is structured as follows: Section 2 describes the VLC system based on DCO-OFDM and the reception method. In Section 3, we propose a BiGRU-based receiver, while experimental validation and result analyses are presented in Section 4. Finally, we conclude the full article in Section 5.