# Bi-LSTM-Augmented Deep Neural Network for Multi-Gbps VCSEL-Based Visible Light Communication Link
## 5 Discussion and Conclusions  
Since VCSELs have been developed quickly in recent decades, a number of studies have shown VCSELs' feasibility in a wide range of applications. In particular, in optical wireless communication and visible light communication, VCSELs are considered one of the most promising light sources due to their low manufacturing cost, high modulation bandwidth and straightforward operation and management. In this work, to meet the multi-Gb/s data rate requirements imposed on the next-generation wireless communication system, the sub-GHz modulation bandwidth of the off-the-shelf VCSELs was successfully compensated for by utilizing the bidirectional long short-term memory-based machine learning scheme. To the best of the authors' knowledge, the highest single channel data rate (13.5 Gbps) with a 2 m link distance from a cost-effective commercial VCSEL device was demonstrated. Additionally, the spectral efficiency (data rate/bandwidth) is much higher than that in previous work. The best result reported in a previous work [21] using an OFDM scheme showed 2.4 bps/Hz; however, the method introduced in this work has an efficiency of 27 bps/Hz, which is ~11 times higher than that previously reported. Future work includes further improving the data rate by applying other modulation and multiplexing schemes and exploring other effective machine learning techniques for VCSEL-based visible light communication networks, considering the efficiency of the machine learning process at the same time. Additionally, in-depth analytic investigations of the chaos-theory-based algorithms with less complexity in order to overcome the complex structure of modern deep neural networks are potential research topics. Conceptualization, S.N. and H.C.; methodology, S.N., H.C., S.O., M.Y. and S.C.; validation, S.O., M.Y. and S.C.; formal analysis, S.O. and M.Y. ; investigation, S.O. and M.Y. and S.C.; resources, S.N. and H.C.; data curation, S.O. and M.Y. ; writing--original draft preparation, S.O. and M.Y. ; writing--review and editing, S.N. and H.C.; visualization, S.O. and M.Y. ; supervision, S.N. and H.C.; project administration, S.N. and H.C.; All authors have read and agreed to the published version of the manuscript. This work was supported by an Incheon National University Research Grant in 2020. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. Figure 10: BER performances (**a**) by the number of units, (**b**) by the number of layers and types. * _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Adversarial Machine Learning in Wireless Communications using RF Data: A Review. _Signal Processing_**2020**, _2012_, 14392. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Role of machine learning and deep learning in securing 5G-driven industrial IoT applications. _Ad Hoc Netw._**2021**, _123_, 102685. [CrossRef]
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