# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 5 Conclusions and Outlook
### Future Outlook  
In response to the challenges currently faced by VLC modulation format recognition technology, as well as the requirements of 6G for global coverage and intelligence, the future development of this technology will focus on two main objectives: enhancing algorithm robustness to improve adaptability to complex channel environments and reducing hardware resource consumption to enable real-time and accurate recognition on mobile devices. The outlook for future solutions can be summarized as follows:  
1. Multi-Domain Feature Fusion and Adaptive Feature Extraction  
Integrate features from multiple domains such as time, frequency, and statistical properties to enhance the ability to distinguish between different modulation formats and reduce dependence on single signal representations. Introduce algorithms like self-attention mechanisms to dynamically allocate feature weights, reducing reliance on manual design and further enhancing the intelligence level of the algorithms. 2. Reducing Computational Complexity and Model Size to Meet Real-Time Requirements  
Explore lightweight neural network architectures, optimize model training strategies, and adopt efficient feature-dimensionality reduction techniques to enable modulation recognition algorithms to run efficiently on resource-constrained embedded devices. Focus on practical deployment with co-design of hardware and software, and optimize the dataflow scheduling and memory access patterns of convolution operations based on the hardware characteristics of mobile NPUs/FPGAs. This will promote the implementation of these algorithms in large-scale application scenarios such as the Internet of Things and vehicular networks. 3. Enhancing Interdisciplinary Technology Integration and Innovation  
For instance, draw on image analysis and signal-processing techniques from the medical field to provide new ideas and methods for the development of VLC modulation format recognition technology. Break through the performance bottlenecks of traditional algorithms to achieve high-precision recognition of complex modulated signals. 4. Feature Enhancement Under Complex Channels  
Design adaptive feature-enhancement strategies based on channel characteristics to automatically adjust the methods of feature extraction and enhancement according to different channel conditions. Maximize the distinguishability of features to further improve modulation recognition performance. (5) Joint Optimization of Channel Estimation and Classifier  
Focus on designing an integrated framework that allows channel estimation and classifiers to be jointly trained and optimized under a unified objective function. Enable channel estimation results to directly guide the learning process of the classifier and use feedback from the classifier to help channel estimation more accurately capture channel characteristics, reducing performance losses due to channel estimation errors and classifier inadaptability. (6) Promoting Model Generalization from Simulation to Real-World Scenarios  
The issues of data scarcity and domain shift can be addressed through simulation-real data collaborative enhancement techniques. Utilize generative adversarial networks (GANs) to synthesize data that conform to actual channel characteristics, and combine this with a small amount of labeled real-world data to construct a semi-supervised training set. Develop unsupervised domain adaptation frameworks that introduce adversarial training mechanisms to eliminate feature distribution differences between the training and deployment domains. Design lightweight feature alignment modules to fine-tune models under unlabeled target domain conditions. Additionally, it is necessary to establish a multi-dimensional evaluation metric system. Beyond traditional recognition accuracy, metrics such as computational latency, energy efficiency ratio, and model interpretability should be given increased importance to drive the technology towards practical application.