The Performance Improvement of VLC-OFDM System Based on Reservoir Computing

Abstract

Nonlinear effects have been restricting the development of high-speed visible light communication (VLC) systems. Neural network (NN) has become an effective means to alleviate the nonlinearity of a VLC system due to its powerful ability to fit complicated functions. However, the complex training process of traditional NN limits its application in high-speed VLC. Without performance penalty, reservoir computing (RC) simplifies the training process of NN by training only part of the network connection weights, and has become an alternative scheme to NN. For the indoor visible light orthogonal frequency division multiplexing (VLC-OFDM) system, this paper studies the signal recovery effect of the pilot-assisted reservoir computing (PA-RC) frequency domain equalization algorithm. The pilot information is added to the feature engineering of RC to improve the accuracy of channel estimation by traditional least squares (LS) algorithm. The performance of 64 quadrature amplitude modulation (QAM) signal under different transmission rates and peak to peak voltage (Vpp) conditions is demonstrated in the experiments. Compared with the traditional frequency domain equalization algorithms, PA-RC can further expand the Vpp range that meets the 7% hard-decision forward error correction (FEC) limit of 3.8 × 10−3. At the rate of 240 Mbps, the BER of the system is reduced by about 90%, and the utilization rate of the available frequency band of the system reaches 100%. The results show that PA-RC can effectively improve the transmission performance of VLC system well, and has strong generalization ability.

Reconstructed Hybrid Optical OFDM-NOMA for Multiuser VLC Systems

“…where min{•} denotes the minimum of the sequence, and mod{} is the operation for calculating the remainder. It can be proved that the reconstruction signal in (12) can guarantee the non-negativity, and is only loaded at the real part of the even-indexed subcarrier, which implies that no interference is imposed on the transmitted symbols. The detailed proof is provided in Appendix A.…”

“…We first prove that the reconstruction signal in (12) can generate the non-negative RHO-OFDM signal. Based on (12), the following inequality holds:…”

“…Furthermore, we will prove that the reconstruction signal is loaded at the real part of the even-indexed subcarrier. According to (12), the reconstruction signal has the following characteristics:…”

“…In VLC, a real and non-negative signal is required due to the intensity-modulated direct detection [11,12]. Therefore, a variety of optical OFDM (O-OFDM) strategies have been conceived for VLC.…”

Transfer Learning Strategy in Neural Network Application for Underwater Visible Light Communication System

“…Machine learning (or more often deep learning) is a powerful fitting tool to solve high-order nonlinearity and there are already studies on this idea [20][21][22]. On the one hand, it is more flexible to set the scale of the NN model by tuning its width and depth, and whether to apply a convolution layer in the model.…”