## 1 Introduction  
With the rapid development of information and communication technologies and the increasing demand for wireless communication, traditional radio frequency spectrum resources are gradually becoming saturated. To address this challenge, researchers have begun exploring new communication spectra, among which Visible Light Communication (VLC) has gained widespread attention due to its abundant spectrum resources, lack of electromagnetic interference, and strong confidentiality. Orthogonal Frequency Division Multiplexing (OFDM), known for its strong inter-symbol interference resistance and ultra-high frequency band utilization, has been successfully applied in radio frequency systems and is also widely used in VLC systems. Unlike radio frequency systems, data in VLC are modulated onto the instantaneous light intensity emitted by Light Emitting Diodes (LEDs) through Intensity Modulation (IM) and directly detected by Photodiodes (PDs). Consequently, all signals in the VLC channel must be real, non-negative, and bounded. Direct Current-biased Optical OFDM (DCO-OFDM) adds a direct current bias and employs Hermitian symmetry to provide non-negative real-valued signals, combining the low-cost structure of Intensity Modulation and Direct Detection (IM/DD). This makes it widely applicable in high-capacity, high-speed VLC systems [1, 2]. In traditional communication systems, data processing requires multiple signal processing modules within both the transmitter and receiver. Although there are mature technologies available for handling relevant signals in both radio frequency and optical communication systems, each module is designed and optimized independently for specific tasks under different assumptions. This approach often results in local optimization but makes achieving global system optimization difficult. Additionally, various communication channel models are typically represented as numerical analysis-based mathematical models. These models often involve significant computational complexity and high time complexity, and the assumptions may not accurately describe the transmission scenarios, thereby significantly reducing the system's transmission performance. To address these issues, end-to-end systems have been introduced in wireless and fiber optic communication systems [3; 4]. This approach transforms the entire communication system into an autoencoder (AE), jointly optimizing the transmitter, channel model, and receiver to achieve an optimized solution for the entire communication system. Meanwhile, deep learning techniques can achieve similar results to numerical analysis methods in many aspects, while significantly reducing the required time [5]. The importance of OFDM for achieving high-capacity VLC systems is self-evident. [PERSON] and colleagues used the Top Samples Detection and Appending (TSDA) technique to reduce the PAPR performance in DCO-OFDM-based VLC systems [6]. [PERSON] et al. studied the impact of modulation order and DC bias voltage on the signal-to-noise ratio (SNR) in DCO-OFDM-based VLC systems [7]. [PERSON] and colleagues proposed a hybrid OFDM-pulse time modulation (PTM) scheme to enhance the bit error rate (BER) performance of DCO-OFDM systems [8]. Several studies have explored communication systems based on end-to-end learning, where traditional transceivers are represented by deep neural networks (DNNs). As depicted in Figure 1b, the transmitter learns to encode the transmitted symbols into coded data \(\mathcal{X}\), which is then sent through the channel, while the receiver learns to recover the transmitted symbols from the received signal \(\mathcal{Y}\). From Figure 1b, it can be seen that updating the DNN requires optimizing a loss function and updating the network parameters. Recently, data-driven methods based on learning have provided a new perspective for dealing with hypothetical channel models [9].