Correction: Zhou et al. Research Progress on Modulation Format Recognition Technology for Visible Light Communication. Photonics 2025, 12, 512
- 1.
- The expression of Equation (7) in Section 2.2 has been revised to enhance its generality. The original simplified formulation placed the summation symbol outside the Bessel function and exponential term, implying an uncommon assumption that the carrier phases of symbols are independently distributed. The updated equation, which refers to Equation (19) in [16], provides a more comprehensive model.
- 2.
- The original expression of Equation (8) represented the GLRT function for a single received sample rather than the overall function. It would be more appropriate to use the overall GLRT likelihood function here.
- 3.
- The expression of Equation (9) has been generalized to better accommodate the various estimation methods applicable in the HLRT method. We have removed and used to represent the estimated value of .
- 4.
- A sentence was misplaced during the translation process. A sentence describing the HLRT method appeared incorrectly in Section 2.3. It has been moved to its proper location in Section 2.4 with the subject corrected to HLRT.
- 5.
- Section 3.1, Paragraph 3 has been revised for greater clarity and technical precision. The original text’s explanation of the distinguishability of QAM/PAM signals was ambiguous; it now clearly states that the cumulant difference arises from the complex nature of the original signal. The symbol for normalized cumulants has been corrected from to . To better describe the experiment in [19], the word “establish” was changed to “testing”. The original term was inappropriate as the threshold was pre-determined and then used for comparison, not established during the simulation. The description of complexity has been refined by removing the mention of Monte Carlo simulations, as its role in the cited work is limited to performance testing and does not affect the algorithm’s operational complexity.
- 6.
- The sentence preceding Equation (14) has been refined to enhance clarity. The previous phrasing could be misinterpreted as implying a temporal action of “reconstructing... before noise interference.” The intended meaning was to convey a state of matching the ideal and noise-free signal constellation. To express this concept more precisely, the text has been streamlined to “matching the ideal constellation.”
- 7.
- The wording concerning the threshold processing function and feature extraction in Section 3.3 has been revised for greater accuracy. The term “adaptive” has been revised to clarify that the threshold is designed for flexible operator selection, not automatic adaptation. And our original description of feature extraction for the method in [21] was inaccurate regarding the information of wavelet domain and statistical measures.
- 8.
- In Section 3.4, the terminology for performance metrics has been standardized by correcting “sensitivity” to “accuracy”. The description of SVM performance was not clear. It has been made more specific to provide a clearer comparison.
- 9.
- The interpretation of the symbols in Equation (18) has been revised. Our original explanation was based on a slight misinterpretation of the variable definitions in the cited work. The text has been corrected to accurately describe the definitions in the original reference.
- 10.
- In our introduction to [25] in Section 4.1, the notation of the signal length has been corrected from to . The description of the model has been refined. Although the convolution kernel used in the work can be abstractly considered as a 3 × 1 temporal convolution kernel, it is now specified as 3 × FI × FO to more accurately reflect the original expression, and the text now clarifies that the channel design is configurable.
- 11.
- A repeated sentence following Equation (22) was deleted, and the word “reconstructs” has been clarified to “transforms” to better describe the process.
- 12.
- In Section 4.1, our description of the work by Gu et al. [29] has been refined for greater accuracy. The term “alternating” was replaced by “utilizing” to clarify the application of two pooling methods. The discussion of robustness was also refined to emphasize the network’s inherent improvement. As for the description of the dropout layer, while its placement in the final layer is a reasonable logical inference based on the model’s structure, this detail was not explicitly stated in the reference. Therefore, it has been omitted to adhere strictly to the reference.
- 13.
- Our description of the work by Wang et al. [31] has been revised for greater accuracy. The “mAP” performance metric is now clearly specified in both the text and Table 4. Furthermore, the parameter count of the improved model has been corrected to 24.17MB, as the previously cited value was for the base model. This required revising the model’s description and updating the parameter reduction calculation to 79.3%.
- 14.
- The wording following Equation (23) has been refined by clarifying the “signal constellation”. It has been made clear that the Hough transform was applied to the constellation diagram.
- 15.
- Our description of the training process and data augmentation of the work by Zhang et al. [34] was inaccurate. It has been revised to more accurately reflect the reference and improve clarity. And "noise injection" is no longer categorized as data augmentation, as it is applied in the communication simulation.
- 16.
- The use of the word “integrate” in our introduction of the work by Zhao et al. could cause ambiguity. We changed it to “introduce” for better clarity.
- 17.
- Our description of the work by Li et al. [37] has been revised for greater precision. In the feature extraction method, the terminology has been corrected from “image” to “map”. Additionally, our originally described voltage condition was relatively conservative; it has been updated to 0.2–1.3 V to more accurately reflect the full performance capabilities demonstrated in [37]’s source figures.
- 18.
- There was a typing error in the symbols following Equation (31). They have been corrected from “ and ” to “ and ”
- 19.
- In our introduction of the dataset of reference [34], our original description of the query set was inaccurate. Unlike the validation set mentioned earlier, the query set contains unknown samples of existing classes, not unknown classes.
- 20.
- Our performance description of the work in [51] has been updated to include necessary conditions for the stated accuracy, making the description more precise.
Reference | Year | Input | Model | Modulation Types | Typical Accuracy (%) | Typical Conditions |
---|---|---|---|---|---|---|
Liu W. et al. [27] | 2020 | Pseudo constellation diagram | GoogLeNet V3 | BPSK, 4/8/16/32/64QAM | 98 | SNR = 15 dB |
Mortada B. et al. [28] | 2022 | Fan-beam constellation diagram | AlexNet | 2/4/8/16PSK, 8/16/32/64QAM | 100 | OSNR = 15 dB |
Gu Y. et al. [29] | 2022 | 2 × 1024 IQ sequence | CNN | OOK, BPSK, QPSK, 16QAM | 99.98 | SNR = 10~30 dB |
Gao W. et al. [30] | 2022 | 2 × 2N matrix | DrCNN | 4/8/16QAM,8PSK, OOK, 16APSK | 98.3 | SNR = 20 dB |
Wang Y. et al. [31] | 2024 | Time-frequency diagram | YOLOv5s | BPSK, QPSK, 8PSK, 16QAM | 0.993(mAP) | SNR = 20 dB |
Arafa N. et al. [32] | 2024 | Hough transform constellation diagram | Pre-Trained AlexNet | QPSK, 8/16PSK, 4/8/16/32/64QAM | 100 | SNR = 12 dB |
Gao W. et al. [33] | 2024 | 4 × N sequence | BiGRU | 2/4/8/16/32/64QAM, 8/16/32/64APSK | >96 | Linear working area |
Zhang L. et al. [34] | 2020 | Constellation diagram | PGML-CNN | 4/8/16/32/64/128/256 QAM, 2/4/8ASK, 2/4/8/16/32 PSK | 95.63 | SNR = 6 dB |
Zhao Z. et al. [36] | 2022 | Contour stellar image | AlexNet-AL | 2/4/8/16/32/64QAM | 88.78 | SNR = 0~15 dB |
Li F. et al. [37] | 2023 | IQ samples processed by coordinate transformation and folding algorithm. | Reservoir Computing | OOK, 4QAM, 8QAM-DIA, 8QAM-CIR, 16APSK, 16QAM | >90 | Linear working area |
Yao L. et al. [39] | 2024 | Constellation diagram | CIKD-CNN | PAM4, QPSK, 8QAM-CIR, 8QAM-DIA, 16/32QAM, 16/32APSK | 100 | Ideal working area |
Zheng X. et al. [40] | 2024 | Constellation diagram | TCN-LSTM + MMAnet | 4/8/16/32/64QAM | 99.2 | SNR = 4 dB |
References
- Zhou, S.; Du, W.; Li, C.; Liu, S.; Li, R. Research Progress on Modulation Format Recognition Technology for Visible Light Communication. Photonics 2025, 12, 512. [Google Scholar] [CrossRef]
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Zhou, S.; Du, W.; Li, C.; Liu, S.; Li, R. Correction: Zhou et al. Research Progress on Modulation Format Recognition Technology for Visible Light Communication. Photonics 2025, 12, 512. Photonics 2025, 12, 761. https://doi.org/10.3390/photonics12080761
Zhou S, Du W, Li C, Liu S, Li R. Correction: Zhou et al. Research Progress on Modulation Format Recognition Technology for Visible Light Communication. Photonics 2025, 12, 512. Photonics. 2025; 12(8):761. https://doi.org/10.3390/photonics12080761
Chicago/Turabian StyleZhou, Shengbang, Weichang Du, Chuanqi Li, Shutian Liu, and Ruiqi Li. 2025. "Correction: Zhou et al. Research Progress on Modulation Format Recognition Technology for Visible Light Communication. Photonics 2025, 12, 512" Photonics 12, no. 8: 761. https://doi.org/10.3390/photonics12080761
APA StyleZhou, S., Du, W., Li, C., Liu, S., & Li, R. (2025). Correction: Zhou et al. Research Progress on Modulation Format Recognition Technology for Visible Light Communication. Photonics 2025, 12, 512. Photonics, 12(8), 761. https://doi.org/10.3390/photonics12080761