The inherent impairments of visible light communication (VLC) in terms of nonlinearity of light-emitting diode (LED) and the optical multipath restrict bit error rate (BER) performance. In this paper, a model-driven deep learning (DL) equalization scheme is proposed to deal with the severe channel impairments. By imitating the block-by-block signal processing block in orthogonal frequency division multiplexing (OFDM) communication, the proposed scheme employs two subnets to replace the signal demodulation module in traditional system for learning the channel nonlinearity and the symbol de-mapping relationship from the training data. In addition, the conventional solution and algorithm are also incorporated into the system architecture to accelerate the convergence speed. After an efficient training, the distorted symbols can be implicitly equalized into the binary bits directly. The results demonstrate that the proposed scheme can address the overall channel impairments efficiently and can recover the original symbols with better BER performance. Moreover, it can still work robustly when the system is complicated by serious distortions and interference, which demonstrates the superiority and validity of the proposed scheme in channel equalization.

This paper addresses the channel impairment to enhance the system performance of visible light communication (VLC). Inspired by the model-solving procedure in the conventional equalizer, the channel impairment compensation is formulated as a spatial memory pattern prediction problem, then we propose efficient deep-learning (DL)-based nonlinear post-equalization, combining the Volterra-aided convolutional neural network (CNN) and long-short term memory (LSTM) neural network, to mitigate the system nonlinearity and then recover the original transmitted signal from the distorted one at the receiver end. The Volterra structure is employed to construct a spatial pattern that can be easily interpreted by the proposed scheme. Then, we take advantage of the CNN to extract the implicit feature of channel impairments and utilize the LSTM to predict the memory sequence. Results demonstrate that the proposed scheme can provide a fairly fast convergence during the training stage and can effectively mitigate the overall nonlinearity of the system at testing. Furthermore, it can recover the original signal accurately and exhibits an excellent bit error rate performance as compared with the conventional equalizer, demonstrating the prospect and validity of this methodology for channel impairment compensation.

Creative idea selection is an important part of the creative process, but the current research on creative idea selection has not attracted enough attention, and this lack is particularly evident in the research on creativity focusing on adolescents. In view of this, in this study, centered on the creative process, we delved into the effect of the construal level on adolescents’ creative idea selection and whether there were domain differences in this effect. The effect of trait-level construals in the general and social creative domains on creative idea selection was examined in Study 1. The results showed that (1) the creativity of the ideas selected by adolescents with high trait-level construals was higher than that of those with low trait-level construals; (2) the creativity and novelty of idea selection in the social creative domain were significantly lower than those in the general creative domain of adolescents. To determine the consistency and stability of the effects of construal level on creative idea selection, Study 2 distinguished between high and low construal levels on the basis of state initiation. It was demonstrated that (1) the creativity and applicability scores for the idea selection of adolescents with high state-level construals were higher than those of adolescents with low state-level construals; (2) the overall creativity and novelty of adolescents’ idea selection in the social creative domain were significantly lower than those in the general creative domain. On the basis of the above results, the following conclusions were drawn in this study: (1) the overall creativity and novelty of adolescents’ idea selection in the social creative domain are lower than those in the general creative domain, but there are inconsistent results with respect to applicability; (2) both high trait- and state-level construals can promote the creativity and novelty of adolescents’ social creative idea selection.