## 1 Introduction  
Recently, visible light communication (VLC) has attracted considerable attention as a short-range communication technology due to its characteristics of higher bandwidth and lower time delay [1]; it is even regarded as one of the next wireless communication technologies (6G) [2]. VLC technology provides opportunities to the applications of indoor positioning, mobile payment and navigation [3]. In most research, VLC needs photodiodes as dedicated receivers, which delays its commercialization. Thus, optical camera communication (OCC) mostly using a charge-coupled device (CCD) or complementary-metal-oxide-semiconductor (CMOS) cameras as the receiver is researched as a specific type of VLC to overcome the issue [4]. CMOS-based commercial optical cameras are being widely utilized in mobile phones with their rapid development; thus, it provides conditions for OCC technology development [5]. The color-shift keying (CSK) modulation scheme recommended by [6] has been widely adopted in RGB-LED-based OCC systems, offering higher communication efficiency. Three bits are represented by one symbol in an 8-CSK modulation scheme [7], which potentially enhances the data rate performance compared with the on-off keying modulation scheme [8]. However, the inter-symbol interference (ISI) will be introduced by the CMOS-based camera's sampling [1], which will significantly degrade the RGB-LED-based OCC system performance. Most conventional decoding algorithms employed for mitigating ISI and inter-channel interference (ICI) [9] do not perform well in higher-speed transmission environments and only offer data rates of 8.64 kbit/s and 17 kbit/s, respectively [10; 11]. More ingenious algorithms are desperately needed for making innovations for conventional sampling and equalization methods. In recent years, deep learning technology represented by convolutional neural network (CNN) and recurrent neural network (RNN) provides methods to solve these problemsfaced by decoding in OCC systems [12]. In the deep learning field, CNN-based neural networks are sought highly for solving the image classification and region-of-interest (ROI) detection problems, since they specialize in features extraction and nonlinear mapping. However, most previous research only focus on using CNNs for ROI detection [13; 14] and equalization [15], which means that the processing of demodulation still requires signal sampling. Conventional signal sampling algorithms are susceptible to ISI and ICI, resulting in performance deterioration. Introducing CNN-based decoding algorithms for extracting features for images received provides a potential solution for avoiding signal sampling and mitigates ISI and ICI. Toward the time-sequence prediction problem, RNN seems to be a more feasible solution. Long-short-term-memory (LSTM)-based RNN [16] is widely used in natural language processing (NLP) [17], emotional analysis [18], and other time-sequence problems. In VLC, an LSTM-based monitor is used to estimate network performance by calculating the signal-to-noise ratio (OSNR) [19]. However, in the field of rolling-shutter-effect (RSE)-based OCC, the application of RNN is still in its infancy, no researchers pay attention to utilizing LSTM-based equalizers, which reflects that most of the existing equalizers only take spatial-wise equalization into consideration. Combining their advantages of feature extraction and learning to store information over time intervals, we provide an appropriate decoding scheme fusion of the function of demodulation and spatial-wise and time-wise equalization. In this work, to our knowledge, we are the first to put forward a CNN-LSTM-based decoding scheme in which CNN serves as an encoder, and LSTM serves as a decoder and equalizer in OCC. The main contributions of this work are summarized as follows:  
* We design a novel neural network named channel-attention-enhanced LSTM (CAE-LSTM-Net) for an OCC decoding system, which is inspired by the channel attention mechanism and LSTM.