Adaptive Diversity Algorithm Based on Block STBC for Massive MIMO Link Misalignment in UWOC Systems

Round 1

Reviewer 1 Report

The authors propose an adaptive diversity algorithm tailored to massive MIMO space-time block coded underwater optical links, in order to improve the communication robustness to errors caused by transmitter-receiver misalignment.

1. The paper should pass through a careful revision since there are several typos and English mistakes along the text. For instance:

- Row 66: “…low cost,,”

- Row 79: “…optical gain” (missing punctuation)

- Row 80 “…underwateroptical…”

- Row 85: “…some researched…”

- Row 152 “…LES’s…”

2. The contents of the manuscript should be re-organized. For instance, details related to simulations such as Table 1 should be reported in the Section 4 and not in the system model section.

3. The paradigm of Massive MIMO represents a hot topic of RF terrestrial communications, and its discussion and application in the underwater context is very challenging. In this regard, it would be interesting to detail the state of the art about underwater massive MIMO, considering both acoustic and optical communications. The following papers may be useful to this aim:

- W. Wu, X. Gao, C. Sun and G. Y. Li, "Shallow Underwater Acoustic Massive MIMO Communications," in IEEE Transactions on Signal Processing, vol. 69, pp. 1124-1139, 2021, doi: 10.1109/TSP.2021.3050037.

- A. Petroni et al., "Hybrid Space-Frequency Access for Underwater Acoustic Networks," in IEEE Access, vol. 10, pp. 23885-23901, 2022, doi: 10.1109/ACCESS.2022.3154105.

- B. M. Lee, "Massive MIMO for Underwater Industrial Internet of Things Networks," in IEEE Internet of Things Journal, vol. 8, no. 20, pp. 15542-15552, 15 Oct.15, 2021, doi: 10.1109/JIOT.2021.3073197.

4. In Equation (2), the dimension of matrix and vectors should be specified.

5. The numerical values employed to describe the distributions in Equation (3) and (5) should be provided.

6. At row 187, the conditions (1), (2) and (3) should be better introduced and discussed.

7. Figure 5 may be removed as it does not carry significant information.

8. The authors declare to use Alamouti STBC algorithm, that was originally developed for the 2x2 MIMO case. So, more details should be added about its implementation in the 16x16 scenario considered in the paper.

9. Figure 7 should be presented at the beginning of Section 3.

10. Regarding Figure 10, the author should deepen the discussion about the behavior of the red curve in the range between 20 and 25 mm of link misalignment.

Author Response

Dear reviewer,

Thank you for your review comments, it helps me a lot with my revision. This paper propose an adaptive diversity approach depending on partition space time block code (STBC). STBC technology is used to reduce the random fading of optical signals caused by turbulence. At the same time, the improvement of channel correlation occurred due to channel misalignment is effectively alleviated by adaptive processing. The adaptive diversity algorithm based on segmented STBC effectively improves the reliability and decrease complexity of underwater Massive MIMO. The adaptive diversity algorithm determines the particular link misalignment degree by the channel matrix obtained from the channel estimation and selects different combinations of detectors according to the degree of misalignment to obtain the maximum gain of the received signal combination. Compared with the chunking scheme, the adaptive diversity algorithm improves the tolerance of the system to the link misalignment error from 30mm to 60mm under the same channel matrix condition, and it can still demodulate the source signal directly without requiring detection algorithm in case of large error in the link misalignment. So, to describe the original contribution clearly, we have carefully revised the paper and have marked the revisions in yellow font. The revisions are listed as follows:

1. The paper should pass through a careful revision since there are several typos and English mistakes along the text.

Yes, we have read the entire article carefully and corrected typos and English mistakes.

2. The contents of the manuscript should be re-organized. For instance, details related to simulations such as Table 1 should be reported in the Section 4 and not in the system model section.

Yes, your suggestion is very good, but section 2.3 derives the underwater turbulence and link misalignment model and uses the ZEMAX software to trace the MIMO optical path according to the parameters in Table 1 and obtains the spot distribution as in Figure 4. The parameters in Table 1 are used to obtain the channel matrix condition number of this channel model using ZEMAX software simulation in order to better describe the underwater turbulence and link misalignment model. Therefore, the Table 1 display is necessary in Section 2.

3. The paradigm of Massive MIMO represents a hot topic of RF terrestrial communications, and its discussion and application in the underwater context is very challenging. In this regard, it would be interesting to detail the state of the art about underwater massive MIMO, considering both acoustic and optical communications.

Yes, we have added the technical status of underwater massive MIMO, considering acoustic and optical communication, and added references 15-17.

4.  In Equation (2), the dimension of matrix and vectors should be specified.

Yes, thank you for your suggestions, we have added the dimensions of the matrix and vectors in Equation (2).  is an  channel matrix,  is a  signal vector of transmitter,  denotes  Gaussian white noise with variance of  and a 0 mean.

5. The numerical values employed to describe the distributions in Equation (3) and (5) should be provided.

Yes, we have added the values used to describe the distributions in Equation (3) and (5).

6. At row 187, the conditions (1), (2) and (3) should be better introduced and discussed.

Yes, your suggestions are of great help to us. We have added to the presentation of the conditions (1), (2) and (3).

In order to better describe the image spot distribution under transceiver alignment and misalignment scenarios, we made the following assumptions : (1) The diameter of the imaging spot is equal to the length of the detector side, and the diameter is , and the imaging spot covers the detector when the transceiver is aligned; (2) The light intensity within the image spot is uniformly distributed; (3) There is a small gap between various detectors. When the link misalignment, the image spot will illuminate adjacent detectors causing interference.

7. Figure 5 may be removed as it does not carry significant information.

Yes, we have removed Figure 5 based on your suggestion.

8. The authors declare to use Alamouti STBC algorithm, that was originally developed for the MIMO case. So, more details should be added about its implementation in the 16x16 scenario considered in the paper.

Yes, your suggestion is very good. We have described in more detail the implementation in the  MIMO scenario in Section 3.

As shown in Figure 3(a), there are 16 LEDs at the transmitter side. Categorize 16 LEDs into four groups of light sources, and each group of optical sources based on four LEDs. STBC technique is used to encode the data transmitted by the four LEDs in each group, and the four groups of optical sources respectively transmit four different signals to form a  Massive MIMO communication system. Therefore, the communication links are divided into four parts, and each part transmits different communication data, and the communication links in each part adopt STBC-EGC technology.

9. Figure 7 should be presented at the beginning of Section 3.

Yes, according to your suggestion, we have shown Figure 7 at the beginning of Section 3 and added text to describe the image, as shown in Figure 6.

10. Regarding Figure 10, the author should deepen the discussion about the behavior of the red curve in the range between 20 and 25 mm of link misalignment.

Yes, Figure 9 shows the condition number of the channel matrix under different misalignment errors using adaptive diversity processing and block diversity processing respectively in the weak turbulence environment. When the degree of link misalignment is small, the condition number of the matrix reconstructed by adaptive diversity is the same as reconstructed by block diversity. When the link misalignment error is greater than 20 mm and the number of channel matrix conditions increases rapidly. The adaptive diversity algorithm reduces the interference between different diversity blocks by changing the detector merging scheme at the at the receiver side. Since the switching decision of the adaptive diversity algorithm for the detector merging scheme is hard, which generates a certain error, it leads to a higher correlation between different diversity blocks when switching the merging scheme compared to the unswitched detector merging scheme. However, with the increase of the link misalignment degree, the condition number of the matrix after adaptive diversity reconstruction is smaller than that of the channel matrix after block diversity reconstruction. Because adaptive diversity determines the degree of link misalignment through the matrix obtained by channel estimation. The receiver side selects a suitable detector merging scheme for the reception of the offset optical signal and the channel condition number is reduced.

Author Response File: Author Response.pdf

Reviewer 2 Report

The topic of Unguided Optical Communication is a hot one thus the presented research is timely. The text is well-written and well-formatted. The authors’ logic and all the derivations are clear. The obtained results are somewhat interesting.

Although, there are several issues with the submission.

1. Recently, the authors published a paper on the same topic:

[1] Channel Model and Signal-Detection Algorithm for the Combined Effects of Turbulence and Link Misalignment in Underwater Optical Massive MIMO Systems,” Journal of Marine Science and Engineering, vol. 11, no. 3, p. 547, Mar. 2023, doi: 10.3390/jmse11030547,

which considers EXACTLY the same model, system setup, and even the text is the same (up to simple word change and reordering). Even the simulation and results part is similar (for example, Fig.7 in [1] and Fig.8 in the current submission are identical). Moreover, the authors did not even include this work in the reference list and did not discuss the improvements in the current submission. This makes me think of self-plagiarism. Which should not be tolerated. The difference that I personally see is in Fig.9-13. Is that enough for a top-cited journal? Definitely no, since it can be considered a marginal contribution.

It is expected that the authors:

– state the results of this research in the introduction, and discuss the improvements in the current submission.

– rewrite the submission, removing the duplicating parts (they can reference their own work if they wish).

Moreover, in view of all of the above (and the issues stated below), the contribution must be clarified! Up to now, it is vanishingly small. It must be addressed in the introduction section, preferably in an itemized list.

2. The literature review is extremely shallow. A lot of major contributions in the field are omitted. The topic of Optical Communications is a very hot one; there is a multitude of research projects (and hence publications) that present results in this area.

Concerning UWOC, there is a special issue with MDPI:

https://www.mdpi.com/journal/jmse/special_issues/YN2AR7OT6O

for example:

[2] Performance Analysis of MIMO-mQAM System with Pointing Errors and Beam Spreading in Underwater Málaga Turbulence Channel, Journal of Marine Science and Engineering, vol. 11, no. 3, p. 633, Mar. 2023, doi: 10.3390/jmse11030633.

3. Moreover, there are some publications that combine UWOC, MIMO and STBC, for instance:

[3] Underwater wireless optical MIMO system with spatial modulation and adaptive power allocation, Optics Communications, vol. 412. Elsevier BV, pp. 21–27, Apr. 2018. doi: 10.1016/j.optcom.2017.12.006.

Thus, the difference between [3] with the submission must be cleared out.

4. The authors almost completely ignored the FSO (free-space optical communication) publications. Although UWOC (underwater) and FSO channels physically are very different, their mathematical and statistical description is very close, sometimes even identical.

[4] Performance Analysis of Free-Space Optical Links Over Málaga (M) Turbulence Channels With Pointing Errors, in IEEE Transactions on Wireless Communications, 2016, doi: 10.1109/TWC.2015.2467386.

[5] Unified Performance Assessment of Optical Wireless Communication Over Multi-Layer Underwater Channels, in IEEE Photonics Journal, 2022, doi: 10.1109/JPHOT.2022.3201081.

5. The channel model that the authors had chosen is the simplest one. There is a wide variety of novel and more general models. So this choice must be addressed, and an overview of the existing channel models (with comparison) is expected.

6. The used term “channel conditions” in conjunction with “channel matrix” is very bad, since “matrix conditioning” is a specific term. Please, do not mess with terminology.

7. In equation (8), the authors artificially perform a bordering of the channel matrix. This is closely related to the so-called banded (or bordered) matrices. In the field of MIMO communication this aspect has been numerously addressed, for example:

[6] Spatial correlation of multiple antenna arrays in wireless communication systems,” Progress In Electromagnetics Research, vol. 132. The Electromagnetics Academy, pp. 347–368, 2012. doi: 10.2528/pier12080604.

[7] Banded correlation matrix model for massive MIMO systems," 2017 IEEE East-West Design & Test Symposium (EWDTS), Novi Sad, Serbia, 2017, pp. 1-6, doi: 10.1109/EWDTS.2017.8110132.

[8] Correlation Matrix Bordering for Optimal Massive MIMO Power Allocation Algorithms," 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW), Riga, Latvia, 2020, pp. 18-23, doi: 10.1109/MTTW51045.2020.9245040.

etc…

So, the difference between the assumed in the submission approach and the one that existed must be addressed.

8. In the “System model” section, you describe a model of a 16×16 MIMO within a 4(?!) meter range. Please, can you elaborate on a plausible scenario (or example), where such a system can be of any use?

9. There are some minor problems with typesetting. Some grammar and punctuation problems are also present and must be fixed in the final version (if accepted)

So, the main question that the authors must address is: “What is the contribution of the proposed research? And how the proposed submission differs from the existing results”. Up to now, it seems to be a self-plagiarism case.

Author Response

Dear reviewer,

Thank you for your review comments, it helps me a lot with my revision. This paper propose an adaptive diversity approach depending on partition space time block code (STBC). STBC technology is used to reduce the random fading of optical signals caused by turbulence. At the same time, the improvement of channel correlation occurred due to channel misalignment is effectively alleviated by adaptive processing. The adaptive diversity algorithm based on segmented STBC effectively improves the reliability and decrease complexity of underwater Massive MIMO. The adaptive diversity algorithm determines the particular link misalignment degree by the channel matrix obtained from the channel estimation and selects different combinations of detectors according to the degree of misalignment to obtain the maximum gain of the received signal combination. Compared with the chunking scheme, the adaptive diversity algorithm improves the tolerance of the system to the link misalignment error from 30mm to 60mm under the same channel matrix condition, and it can still demodulate the source signal directly without requiring detection algorithm in case of large error in the link misalignment. So, to describe the original contribution clearly, we have carefully revised the paper and have marked the revisions in yellow font. The revisions are listed as follows:

1. Recently, the authors published a paper on the same topic:

[1] Channel Model and Signal-Detection Algorithm for the Combined Effects of Turbulence and Link Misalignment in Underwater Optical Massive MIMO Systems,” Journal of Marine Science and Engineering, vol. 11, no. 3, p. 547, Mar. 2023, doi: 10.3390/jmse11030547,

which considers EXACTLY the same model, system setup, and even the text is the same (up to simple word change and reordering). Even the simulation and results part is similar (for example, Fig.7 in [1] and Fig.8 in the current submission are identical). Moreover, the authors did not even include this work in the reference list and did not discuss the improvements in the current submission. This makes me think of self-plagiarism. Which should not be tolerated. The difference that I personally see is in Fig.9-13. Is that enough for a top-cited journal? Definitely no, since it can be considered a marginal contribution.

It is expected that the authors:

– state the results of this research in the introduction, and discuss the improvements in the current submission.

– rewrite the submission, removing the duplicating parts (they can reference their own work if they wish).

Moreover, in view of all of the above (and the issues stated below), the contribution must be clarified! Up to now, it is vanishingly small. It must be addressed in the introduction section, preferably in an itemized list.

Yes, thank you for your suggestion. We have included the literature you provided [1] in our reference list [32]. And we have explained the difference between what we have studied and what is in the literature [32]. The contribution of this study is presented in the introduction. The literature [32] and the current paper's employ Massive MIMO systems under the combined effects of turbulence and link misalignment. However, while the literature [32] focuses on anti-interference detection algorithms, this paper focuses on adaptive diversity algorithms for Massive MIMO systems. Therefore the channel models of the two articles are similar, and we will remove Figure 8 as you suggested, reworking the whole article and not simply changing and reordering the words.

In [32], an improved order successive interference cancellation (I-OSIC) algorithm based on the partitioned STBC technique is proposed. However, with a large degree of channel misalignment, if the detectors at the receiver side are combined in a group of four chunks, it rather enhances the interference between the signals. However, using the detection algorithm at the receiver side increases the complexity of signal detection.

In this paper, a partition STBC adaptive diversity algorithm is proposed for the joint effect caused by link misalignment and turbulence. The signal can be effectively detected at the receiver side using a low-complexity detection algorithm. The adaptive diversity algorithm depending on partition STBC reduce the influence of random attenuation of optical signal occurred due to turbulence on system reliability by sacrificing part of communication links, and also reduce the influence of aggravation of inter-channel interference caused by link misalignment. Our main contributions are summarized in the following:

• The imaging spot displacement phenomenon caused by the combined effect of turbulence and link misalignment can cause interference to adjacent detectors in Massive MIMO system. To address this issue, we propose a signal adaptive diversity system in Massive MIMO system, which the signals received by different detectors are combined according to the size of the interference.
• Based on the optical Massive MIMO adaptive diversity system, we propose an evaluation model and a method to judge the degree of link misalignment. And the optimal merging scheme can be found by this method.
• Further, we propose an adaptive diversity algorithm in the adaptive diversity system and the optimal merging model, which the receiver adopts EGC combining technology to combine the signals received by different detectors according to the size of the interference. It will resist the effects of the joint effects of turbulence and link misalignment.

2. The literature review is extremely shallow. A lot of major contributions in the field are omitted. The topic of Optical Communications is a very hot one; there is a multitude of research projects (and hence publications) that present results in this area.

Concerning UWOC, there is a special issue with MDPI:

https://www.mdpi.com/journal/jmse/special_issues/YN2AR7OT6O

for example:

[2] Performance Analysis of MIMO-mQAM System with Pointing Errors and Beam Spreading in Underwater Málaga Turbulence Channel, Journal of Marine Science and Engineering, vol. 11, no. 3, p. 633, Mar. 2023, doi: 10.3390/jmse11030633.

Yes, we have added to the description of research in the field of optical communication and included the literature in references [24].

3. Moreover, there are some publications that combine UWOC, MIMO and STBC, for instance:

[3] Underwater wireless optical MIMO system with spatial modulation and adaptive power allocation, Optics Communications, vol. 412. Elsevier BV, pp. 21–27, Apr. 2018. doi: 10.1016/j.optcom.2017.12.006.

Thus, the difference between [3] with the submission must be cleared out.

Yes, your suggestion is very good. We have added the description of the study combining UWOC, MIMO and STBC. And included the literature in references [21].

4. The authors almost completely ignored the FSO (free-space optical communication) publications. Although UWOC (underwater) and FSO channels physically are very different, their mathematical and statistical description is very close, sometimes even identical.

[4] Performance Analysis of Free-Space Optical Links Over Málaga (M) Turbulence Channels With Pointing Errors, in IEEE Transactions on Wireless Communications, 2016, doi: 10.1109/TWC.2015.2467386.

[5] Unified Performance Assessment of Optical Wireless Communication Over Multi-Layer Underwater Channels, in IEEE Photonics Journal, 2022, doi: 10.1109/JPHOT.2022.3201081.

Yes, we have added to the research introduction of free space optical communication. And included the literature in references [26] and [27].

5. The channel model that the authors had chosen is the simplest one. There is a wide variety of novel and more general models. So this choice must be addressed, and an overview of the existing channel models (with comparison) is expected.

Yes, thank you for your suggestion. However, our research mainly focuses on modeling large-scale MIMO link misalignment channels and how to solve the interference problem caused by increased signal correlation. In future research, we will consider more novel underwater turbulent channel models.

6.The used term “channel conditions” in conjunction with “channel matrix” is very bad, since “matrix conditioning” is a specific term. Please, do not mess with terminology.

Yes, your suggestion is very good. We have changed the “channel conditions” to the “condition number of channel gain matrix”, and changed the “channel matrix” to the “channel gain matrix”.

7. In equation (8), the authors artificially perform a bordering of the channel matrix. This is closely related to the so-called banded (or bordered) matrices. In the field of MIMO communication this aspect has been numerously addressed, for example:

[6] Spatial correlation of multiple antenna arrays in wireless communication systems,” Progress In Electromagnetics Research, vol. 132. The Electromagnetics Academy, pp. 347–368, 2012. doi: 10.2528/pier12080604.

[7] Banded correlation matrix model for massive MIMO systems," 2017 IEEE East-West Design & Test Symposium (EWDTS), Novi Sad, Serbia, 2017, pp. 1-6, doi: 10.1109/EWDTS.2017.8110132.

[8] Correlation Matrix Bordering for Optimal Massive MIMO Power Allocation Algorithms," 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW), Riga, Latvia, 2020, pp. 18-23, doi: 10.1109/MTTW51045.2020.9245040.

etc…

So, the difference between the assumed in the submission approach and the one that existed must be addressed.

Yes, thank you for your suggestion. As described in references [6]-[8], spatial correlation occurs when the array element spacing of the antenna array in a wireless RF MIMO system is less than half a wavelength. In an optical MIMO system, the beams from different LEDs are largely separated from each other by the imaging lens at the receiver end, and the channel matrix can be expressed as the elements on the diagonal are much larger than the elements on the non-diagonal. At this point, the interference between different LED beams is mainly caused by the spot shift due to the link misalignment. Therefore, the movement of the spot in different directions will result in interference caused by the irradiation of different spots onto the same detector. There is a fundamental difference between the two.

8. In the “System model” section, you describe a model of a 16×16 MIMO within a 4(m) range. Please, can you elaborate on a plausible scenario (or example), where such a system can be of any use?

Yes. For example, when underwater AUVs communicate with underwater nodes, a short-range, high-capacity communication system is required. In this case, an optical  Massive MIMO communication system with a communication distance of 4m can be applied in this scenario.

9. There are some minor problems with typesetting. Some grammar and punctuation problems are also present and must be fixed in the final version (if accepted).

So, the main question that the authors must address is: “What is the contribution of the proposed research? And how the proposed submission differs from the existing results”. Up to now, it seems to be a self-plagiarism case.

Yes, we have read the entire article carefully and corrected typos and English mistakes. And distinguishes the proposed submission from existing results.

In [32], an improved order successive interference cancellation (I-OSIC) algorithm based on the partitioned STBC technique is proposed.

In this paper, a partition STBC adaptive diversity algorithm is proposed for the joint effect caused by link misalignment and turbulence.

Our main contributions are summarized in the following:

• The imaging spot displacement phenomenon caused by the combined effect of turbulence and link misalignment can cause interference to adjacent detectors in Massive MIMO system. To address this issue, we propose a signal adaptive diversity system in Massive MIMO system, which the signals received by different detectors are combined according to the size of the interference.
• Based on the optical Massive MIMO adaptive diversity system, we propose an evaluation model and a method to judge the degree of link misalignment. And the optimal merging scheme can be found by this method.
• Further, we propose an adaptive diversity algorithm in the adaptive diversity system and the optimal merging model, which the receiver adopts EGC combining technology to combine the signals received by different detectors according to the size of the interference. It will resist the effects of the joint effects of turbulence and link misalignment.

Author Response File: Author Response.pdf

Reviewer 3 Report

This manuscript proposes an adaptive diversity approach depending on partition space time block code (STBC) to reduce the random fading of optical signals caused by turbulence. The manuscript has been well-written. However, I have the following comments: 

- Please provide your specific contributions as a bullet list point at the end of the Introduction, and then, provide the structure of your paper.

- At the end of the Conclusion, it is needed to discuss the limitations of this work, and accordingly, provide some related future research directions to alleviate these limitations.

Author Response

Dear reviewer,

     Thank you for your review comments, it helps me a lot with my revision. This paper propose an adaptive diversity approach depending on partition space time block code (STBC). STBC technology is used to reduce the random fading of optical signals caused by turbulence. At the same time, the improvement of channel correlation occurred due to channel misalignment is effectively alleviated by adaptive processing. The adaptive diversity algorithm based on segmented STBC effectively improves the reliability and decrease complexity of underwater Massive MIMO. The adaptive diversity algorithm determines the particular link misalignment degree by the channel matrix obtained from the channel estimation and selects different combinations of detectors according to the degree of misalignment to obtain the maximum gain of the received signal combination. Compared with the chunking scheme, the adaptive diversity algorithm improves the tolerance of the system to the link misalignment error from 30mm to 60mm under the same channel matrix condition, and it can still demodulate the source signal directly without requiring detection algorithm in case of large error in the link misalignment. So, to describe the original contribution clearly, we have carefully revised the paper and have marked the revisions in yellow font. The revisions are listed as follows:

1. Please provide your specific contributions as a bullet list point at the end of the Introduction, and then, provide the structure of your paper.

Yes, we have added our specific contributions in the form of a list at the end of the introduction and provided the structure of the paper.

Our main contributions are summarized in the following:

• The imaging spot displacement phenomenon caused by the combined effect of turbulence and link misalignment can cause interference to adjacent detectors. To address this issue, we propose an adaptive diversity system.
• Based on the adaptive diversity system, we give the model of the optimal merging scheme.
• Based on the adaptive diversity system and the optimal merging model, we propose an adaptive diversity algorithm to resist the effects of the joint effects of turbulence and link misalignment.

The rest of this paper is organized as follows. In Section 2, we provide MIMO-ACO-OFDM based underwater imaging optical system and underwater imaging optical MIMO channel model. The adaptive diversity algorithm model is presented in Section 3. The simulation result analysis is presented in Section 4. Our conclusions are presented in Section 5.

2. At the end of the Conclusion, it is needed to discuss the limitations of this work, and accordingly, provide some related future research directions to alleviate these limitations.

Yes, we have discussed the limitations of this work at the end of the conclusion and accordingly provided some relevant future research directions to alleviate these limitations.

The adaptive diversity algorithm proposed in this paper mainly aims at the problem of spot migration caused by link misalignment. According to different link misalignment degrees, different detector combining schemes are used to receive optical signals. However, when the link misalignment error is large enough, the light spot will deviate from the limit range received by the detector. At this time, the detector is already unable to receive the signal. In future research, we can try to regulate the transmitter angle according to the link misalignment error to reduce the interference effect caused by link misalignment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have addressed all the reviewer's previous comments, improving the quality of the manuscript.

A further revision for language check is suggested.

Reviewer 2 Report

The authors had addressed most of my concerns. I have no further objections.