AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
Abstract
:1. Introduction
2. Statues and Challenges of VLC
2.1. Visible Light Communication E2E Channel
2.2. Modulation Format in VLC
2.3. Advantages and Disadvantages of ML in VLC
3. Machine Learning in Physical Layer of IVLC
3.1. Channel Emulator
3.1.1. TTHNet
3.1.2. FFDNet
3.1.3. Conclusions
3.2. Channel Equalization
3.2.1. Pre-Equalization GK-DNN
3.2.2. Postequalization GK-DNN
3.2.3. Postequalization FSDNN
3.2.4. Postequalization TFDNet
3.2.5. Postequalization DBMLP
3.2.6. Post-Equalization PCVNN
3.2.7. Postequalization LSTM-Equalizer
3.2.8. Postequalization MPANN
3.2.9. Conclusions
3.3. Optimal Decision
3.3.1. K-Means
3.3.2. DBSCAN
3.3.3. GMM
3.3.4. SVM
3.3.5. ANN
3.3.6. Conclusions
3.4. MIMO
3.4.1. ICA
3.4.2. MIMO-MBNN
3.4.3. Joint Spatial and Temporal ANN Equalizer
3.4.4. Adaptive ANN Equalizer
3.4.5. Conclusions
3.5. Optimal Coding
3.5.1. VLC-Based Autoencoder
3.5.2. Fiber/Wireless-Based Autoencoder
3.5.3. Conclusions
4. Future Trend of ML in IVLC
4.1. Intelligent Physical Layer
4.1.1. Fundamental Electromagnetism Theory and Frontiers in Optical Physics
4.1.2. Distributed Channel Equalization
4.1.3. Modulation Format Recognition
4.2. Intelligent Network Layer
4.2.1. Converged Communication and Sensing
4.2.2. Heterogeneous Network
4.2.3. Security Network
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Challenges | Reasons | References |
---|---|---|
Optoelectronic and electro-optical conversion | Introduces additional nonlinearity | [26,27,28,29] |
Large signals | Brings the device into the nonlinear region | [21] |
Wide bandwidth | Introduces severe ISI | [19] |
Different transmission channel modeling | Diverse application scenarios, such as indoor, underwater | [31,32,33] |
Algorithm | Input Layer | 1st Weight Layer | 2nd Weight Layer | Drd Weight Layer | Trainable Parameters |
---|---|---|---|---|---|
MLP (general) | |||||
Volterra (general) | / | / | / |
Equalizers | GK-DNN | FSDNN | TFDNet | MPANN | DBMLP | PCVNN | LSTM |
---|---|---|---|---|---|---|---|
Main types of NN | MLP | MLP | MLP | MLP | MLP | MLP | RNN |
Number of hidden layers | 2 | 1 | 1 | 1 | 1 | 1 | 1 |
Activation function | ReLU | ReLU | ReLU | ReLU | Tanh | ReLU | Tanh, Sigmoid |
Optimizer | Adagrad | Adam | Adam | Adam | Adam | Adam | Adam |
Complexity | Moderate | Low | High | Low | High | Low | High |
Convergence speed | Fast | Moderate | Moderate | Moderate | Slow | Slow | Slow |
Pre-equ. | ✓ | ||||||
Post-equ. | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Deployment location | Waveform | Waveform | Waveform | Waveform | Waveform | Symbol | Symbol |
Algorithms | Supervision | Computational Complexity | Application |
---|---|---|---|
K-means | N | Low | Low nonlinearity |
DBSCAN | N | Low | Time varying |
GMM | Y | High | Moderate nonlinearity, ISI |
SVM | Y | Moderate | Moderate nonlinearity |
ANN | Y | High | High nonlinearity |
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Shi, J.; Niu, W.; Ha, Y.; Xu, Z.; Li, Z.; Yu, S.; Chi, N. AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future. Photonics 2022, 9, 529. https://doi.org/10.3390/photonics9080529
Shi J, Niu W, Ha Y, Xu Z, Li Z, Yu S, Chi N. AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future. Photonics. 2022; 9(8):529. https://doi.org/10.3390/photonics9080529
Chicago/Turabian StyleShi, Jianyang, Wenqing Niu, Yinaer Ha, Zengyi Xu, Ziwei Li, Shaohua Yu, and Nan Chi. 2022. "AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future" Photonics 9, no. 8: 529. https://doi.org/10.3390/photonics9080529
APA StyleShi, J., Niu, W., Ha, Y., Xu, Z., Li, Z., Yu, S., & Chi, N. (2022). AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future. Photonics, 9(8), 529. https://doi.org/10.3390/photonics9080529