Modulation classification (MC) has become a widely used technology, which is of great value in both commercial and civil applications. It actually completes the classification task of modulation signal through various means. In recent years, modulation format recognition based on deep learning (DL) has achieved great success. However, in practical application, the computational cost and model complexity have become the biggest obstacles of the traditional MC based on DL. To solve this problem, we propose complementary folding algorithm (CFA). This is an algorithm based on classical modulation classification (CMC), which folds and splices the features of the input neural network (NN), so that these features have both large-scale and small-scale dual branch receptive fields. The research results prove that under the same network structure and data quantity, both the correctness rate and the convergence speed of CFA are significantly improved in the communication experiment based on underwater visible light communication (UVLC). It is also worth mentioning that because of the particularity and complexity of the channel, UVLC system can be divided into different regions. In any region, CFA performs better than CMC, so we can prove that this algorithm also has excellent robustness.
Visible light communication (VLC) has emerged as a promising communication method in 6G, particularly in the domain of underwater communication. However, because of its intimate coupling to the lightemitting diodes (LED), the transmission rate is limited by the modulation bandwidth and the nonlinearity of the LED. With current device development, LED bandwidth is substantially lower than that of other optical communication devices, necessitating improved spectral efficiency and nonlinearity robustness to achieve high-speed transmission. In this paper, we propose a hexagonal constellation-based geometrically shaped (GS) 32QAM with carrierless amplitude and phase (CAP) modulation for the underwater VLC system. A comprehensive performance comparison of four GS modulation methods, including our proposed hexagonal-32QAM, normal 32QAM, square-32QAM and 32APSK is investigated in both theory and experiment. The hexagonal-32QAM with a modified binary switching coding (BSC) can obtain a Q factor gain of 0.44 dB, and a data rate improvement of 25 Mb/s compared with Gary coding based normal 32QAM. The experimentally results illustrate the feasibility of geometric shaping 32QAM in underwater visible light communication.
As 6G research progresses, both visible light communication (VLC) and artificial intelligence (AI) become important components, which makes them appear to converge. Neural networks (NN) as equalizers are gradually occupying an increasingly important position in the research of the physical layer of VLC, especially in nonlinear compensation. In this paper, we will propose three categories of neural network equalizers, including input data reconfiguration NN, network reconfiguration NN and loss function reconfiguration NN. We give the definitions of these three neural networks and their applications in VLC systems. This work allows the reader to have a clearer understanding and future trends of neural networks in visible light communication, especially in terms of equalizers.
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