# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 1 Introduction  
Visible light communication (VLC) based on light emitting diodes (LEDs) is a promising technology for indoor wireless access [1; 2; 3]. To overcome the multipath distortion caused by reflections from different sources inside a room and enhance the communication efficiency, the optical orthogonal frequency division multiplexing (OFDM) has been widely adopted in VLC systems [4; 5; 6; 7]. However, the high peak to average power ratio (PAPR) associated with the OFDM signals is one of the main limitations for VLC systems due to the constraints on the average radiated optical power and the limited dynamic range of the front-end devices, like digital to analog converters and power amplifiers [8]. High PAPR makes the VLC system more susceptible to non-linear distortions and consequently drastically degrade the system's performance [9; 10; 11]. Several PAPR reduction techniques for DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) systems have been investigated in the literature. The authors proposed a genetic algorithm and peak-value optimization algorithm [12] to mitigate the high PAPR in VLC systems with lower complexity. The study in [13] used a semidefinite relaxation method for tone injection to reduce the PAPR for the DCO-OFDM. The branch and bound method [14] and tone reservation method [15] were also proposed to lower the impact of the high PAPR in VLC systems. Nevertheless, these methods reduce the PAPR at the sacrifice of computational complexity and channel resources. Moreover, the authors in [16] introduced a pilot-assisted approach to achieve improved PAPR performance over the select mapping scheme for high-level constellations. However, it resulted in a data rate loss according to the density of pilot symbols. In addition, a subcarrier grouping scheme for the OFDM-based VLC system [17] is proposed to reduce the PAPR, but at a lower signal-to-noise ratio (SNR) the bit error rate (BER) performance was inferior to that of DCO-OFDM. To mitigate the effects of these limitations, deep learning offers an efficient option for its good generalization properties with flexible modeling and learning capabilities. Deep learning is a promising technique to solve difficult communication problems [18] for its minimal complicity, adaptive hardware and robustness in the analysis of the unknown or complex channels. Use of deep learning methods to solve difficult communication problems has been reported and has demonstrated better performances than conventional communication methods in the end-to-end learning of encoding and decoding application [19]. Another approach reported in [20] is the end-to-end learning of a prototype consisting of two software-defined radios that communicate over an actual wireless channel. Deep learning techniques for channel estimation and symbol detection in an end-to-end manner [21] are investigated which have wide application in many communication systems. Among them, a special network architecture named autoencoder (AE) which is usually used for denoising corrupted data is suitable to dealing with the non-linear distortions caused by the high PAPR [22]. It optimizes reconstruction loss through a series of representations typically using a mean squared error objective and a stochastic gradient descent solver to find network weights achieving an effective regression [23]. An AE-based system [24] that was solely composed of Neural Networks communicating over-the-air was extended in the OFDM scheme. [PERSON] and [PERSON] [22] proposed a PAPR-reducing network and discussed the PAPR behavior in the RF system. [PERSON] and [PERSON] [25; 26] applied an artificial neural network to reduce the complexity in solving the PAPR reduction problem. However, the focus of these studies was limited to the clipping and filtering technique and active constellation extension signals, which may not acquire better performance than conventional methods when extended to optical OFDM systems.