In this paper, we construct the autoencoder (AE) for optical wireless communication (OWC) systems with non-negativity and peak power constraints, which provides effective transceiver design in log-normal channel. We consider the cases where perfect channel state information (CSI) or noisy CSI can be obtained under three kinds of communication rate, which is defined as the ratio of channel use number to bit number. Meanwhile, we present the block error rate (BLER) performance to further demonstrate our transceivers' superior performance than common model-based methods. The learned constellation points distribution is provided to understand the transmitter's performance. Numerical simulations are conducted to ensure the best convergence. The results indicate that AE-based transceivers can achieve model-based optimal BLER performance or provide significantly better BLER performance.

A quadrichromatic light-emitting diode (QLED) based visible light communication for mobile phone camera is proposed to improve data rate and enhance illumination effect at the same time. Different from color intensity modulation (CIM), we propose and use color ratio modulation (CRM) in CMOS image sensor based visible light communication to improve data rate. According to the spectral power distribution (SPD) of the QLED and the spectral response of the complementary-metal-oxide-semiconductor (CMOS) image sensor, color multiple-input multiple-output (CMIMO) channel model is set up first to obtain optimal 16-CRM constellation design. Taking full consideration of the high quality of color rendering index (CRI), tunable color temperature (CT), we design a specific data packet structure to realize illumination requirements. A decoding strategy is also addressed for demapping at the receiver. The experimental results demonstrate that the proposed scheme can realize a downlink data rate of 13.2kbit/s, meanwhile, the optical signal source is illumination compatible.

In this letter, we construct the neural network (NN)-based transceiver to compensate for the varying inter-symbol-interference (ISI) effect in visible light communication (VLC) systems. For processing variable-length sequences, the convolution neural network (CNN) is utilized, and then the residual network structure is further leveraged at the receiver part to enhance the performance. To cope with varying ISI, the pilot sequence, instead of channel side information (CSI) obtained by an additional module, is integrated into the framework to recover the data sequence directly. Simulation results show that the symbol error rate (SER) performance of the proposed NN-based transceiver can outperform separately designed transceiver schemes and approach the ideal perfect CSI (PCSI) case with a few pilot symbols or even no pilot.

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