# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Prospects for the Practical Hardware Deployment of VLC MFR
The superimposed interference and time-varying characteristics of natural and artificial light sources may cause a sharp drop in the SNR of the received signal, requiring hardware accelerators to dynamically adjust quantization precision and decision thresholds. The adaptability of datasets poses the fourth challenge. Before loading the MFR model onto a hardware platform, the classification model is typically pre-trained on a computer based on a given dataset. However, during the actual deployment of VLC hardware terminals, different channel scenarios are often encountered, such as indoor and outdoor scenarios, stationary and mobile scenarios, and scenarios with little or complex occlusion. Models trained offline based on a fixed dataset may face unstable performance in these changing situations. Power consumption optimization is particularly prominent in battery-powered portable devices. Compared with RF hardware systems, the unique dimming control function of VLC systems may add extra energy consumption costs. The lack of standardization further increases deployment difficulties. Differences in optoelectronic interface timing and modulation parameter configuration among VLC module solutions from different vendors lead to the need for recognition algorithms to be compatible with multiple physical layer specifications. Potential solutions to the aforementioned challenges include developing FPGA architectures that support dynamic partial reconfiguration (DPR) to enable algorithm hot swapping; designing optoelectronic collaborative hybrid-precision quantization strategiesthat use low-precision inference for accelerated processing during stable lighting conditions and switch to high-precision mode when the channel degrades; building dedicated processing units based on RISC-V instruction set extensions to optimize convolution operations and activation function calculations through custom instructions; and promoting the formation of industry alliances to draft VLC standards that include modulation recognition interfaces, clarifying forward compatibility requirements. Additionally, promoting the application of transfer learning, active learning, and unsupervised learning techniques in the hardware implementation of VLC-MFR, developing mobile VLC-MFR model hardware implementations that can autonomously train and optimize based on real-world environments, enhancing the environmental adaptability of VLC device deployment, and reducing maintenance costs. These explorations need to form an iterative closed loop between laboratory prototyping and field testing, ultimately driving the transition of MFR technology from algorithm simulation to product deployment. To address these challenges, several potential solutions are proposed. First, developing FPGA architectures that support dynamic partial reconfiguration (DPR) can enable algorithm hot swapping. This allows the hardware to dynamically adapt to different processing needs without requiring full reconfiguration. Second, designing optoelectronic collaborative hybrid-precision quantization strategies is essential. These strategies would use low-precision inference for accelerated processing during stable lighting conditions and switch to high-precision mode when the channel degrades. Moreover, promoting the formation of industry alliances to draft VLC standards that include modulation recognition interfaces is crucial. Clarifying forward-compatibility requirements will ensure that future developments remain aligned with current standards. Additionally, promoting the application of transfer learning, active learning, and unsupervised learning in the hardware implementation of VLC-MFR can lead to the development of mobile VLC-MFR model hardware implementations. These implementations can autonomously train and optimize based on real-world environments, enhancing the environmental adaptability of VLC device deployment and reducing maintenance costs. These explorations need to form an iterative closed loop between laboratory prototyping and field testing.