## 6 Conclusions  
The key insights gained from this empirical study include the following: (a) It is feasible to use DNN for demodulation in optical camera communication, however, the robustness of the model is largely dependent on quality, size and diversity of the dataset. DNN cannot easily extract subtle features from LED ON/OFF signals as they appear as blobs with minimal or no structural feature. Novel spatio-temporal techniques that study the LED signal reception variability across space and time must be incorporated to build novel DNN models for VLC. Hardware inconsistencies (b) in the camera and computing unit (e.g., Raspberry Pi version) can severely impact the performance of DNN demodulation as transmission and reception in VLC are largely incoherent (transmission and receiver samplings are not synchronized). Identifying the exact head and tail of each packet and the detection/estimation of the missed camera frame (samples) are key for improving demodulation accuracy.  
In conclusion, this study has provided a baseline for further work in incorporating DNNs for camera based VLC system that serve as a demodulation mechanism of the transmitted data from the LED. This study has evaluated the performance of various DL/ML models to explore the potential of these models in deciphering an LED state from a regular binary state up to four levels of intensity. Then, to verify these model's performance, a trivial method to solving the same problem was explored to compare the model's performance. This study showed that, with minimal training, the models are able to achieve between 95% to 99% accuracy in classifying the LED intensity/state with respect to the number of intensities. The empirical evaluations using a prototype OCC system showed that DNNs are indeed a very useful for demodulation but require an understanding of what the model is learning in order to effectively create a robust system. Hardware issues presented a major challenge and illustrated the need for specific hardware for this type of communication network. The results from the empirical study demonstrate the effective use of DNNs but only in a set environment; therefore, a dynamic setting requires further model training and perhaps large datasets, etc. Through the course of this study, we have addressed feasibility questions for DNN usage in camera communication. However, this work has opened up new avenues for research questions across the design, development and implementation of novel DNN models and DNN-inspired algorithms for improving receiver performance in camera communication.  
Conceptualization and methodology, A.A. ([PERSON]) and A.A. ([PERSON]); part supervision, [PERSON] ([PERSON]) and M.R.R.; investigation and original draft preparation, [PERSON] ([PERSON]); software, experiment and writing assistance, S.T.V. and M.R.R. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Trained model validated against hold out set.  
**Funding:** This research was funded by National Science Foundation (NSF), grant numbers 1901133 and 2000475.  
**Data Availability Statement:** We have made the raw data used in this paper available here: [[https://realityawarenetworks.com](https://realityawarenetworks.com)]([https://realityawarenetworks.com](https://realityawarenetworks.com)) (accessed on 19 May 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.