# A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
## 8 Conclusions  
In this work, a new OFDM format, DPO scheme, is proposed. In DPO, the two components DCO and PAM-DMT are carried by the odd subcarriers and the imaginary part of the even subcarriers, respectively. A mathematical expression for the DC-bias level is derived considering a target dimming level for DPO. The simulation results show that to reach a BER of \(10^{-3}\), DPO requires almost 2 dB less electrical power than HDAP, as well as 7 dB less electrical power than AHO. Moreover, DPO has great spectral efficiency and a wider dimming range of 8-92%, close to HDAP. Therefore, it provides better performance than HDAP and AHO in terms of electrical power and spectral efficiency. Moreover, this paper introduces a switching scheme for HDAP where the individual components of HDAP are switched in obtaining a wide dimming range at an excellent spectral efficiency. Finally, this paper applies ML regression algorithms to find the constellation sizes of DPO elements. It is shown here that for a given target dimming level, polynomial regression of order 4 can successfully determine the required constellation size of one element of DPO if the other constellation size of the other element is known.  
In the future, ML algorithms can also be used to predict the LiFi channels. Furthermore, the performance of DPO can be evaluated considering other channel impairments, including co-channel interference and side effect modulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Regression Algorithm** & **Testing\%** & **R\({}^{2}\) Score** & **RMSE** \\ \hline Linear regression & 10\% & 31.30\% & 7.30 \\ Polynomial regression degree 2 & 10\% & 87.34\% & 3.13 \\ Polynomial regression degree 3 & 10\% & 97.17\% & 1.48 \\ Polynomial regression degree 4 & 10\% & 98.18\% & 1.18 \\ Polynomial regression degree 5 & 10\% & 30.99\% & 7.31 \\ Polynomial regression degree 2 & 20\% & 89.85\% & 3.07 \\ Polynomial regression degree 3 & 20\% & 95.72\% & 1.99 \\ Polynomial regression degree 4 & 20\% & 97.53\% & 1.51 \\ Polynomial regression degree 2 & 30\% & 86.77\% & 3.39 \\ Polynomial regression degree 3 & 30\% & 93.90\% & 2.30 \\ Polynomial regression degree 4 & 30\% & 95.26\% & 2.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of different regression algorithms.  
Figure 13: Predicted versus actual \(M_{DCO}\) of dc-biased pulse amplitude modulated orthogonal frequency division multiplexing (DPO-OFDM) for the case of polynomial regression of degree = 4. Note that \(M_{DCO}\) stands for the constellation size of DC-biased optical OFDM (DCO-OFDM).  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/electronics10172103/s1](https://www.mdpi.com/article/10.3390/electronics10172103/s1)]([https://www.mdpi.com/article/10.3390/electronics10172103/s1](https://www.mdpi.com/article/10.3390/electronics10172103/s1)), Table S1: Dataset for DPO-OFDM.  
**Author Contributions:** Conceptualization, M.R.H.M.; methodology, I.N., M.R.H.M. and R.I.; software, I.N. and R.I.; validation, I.N. and R.I.; formal analysis, I.N., M.R.H.M. and R.I.; investigation, I.N., M.R.H.M. and R.I.; data curation, I.N., M.R.H.M. and R.I.; writing--original draft preparation, I.N.; writing--review and editing, M.R.H.M. and J.K.; visualization, I.N. and R.I.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding. The APC will be funded by the School of Engineering, IT and Physical Sciences, Federation University Australia.  
**Data Availability Statement:** The dataset generated in this study is uploaded as Supplementary Material.  
**Conflicts of Interest:** The authors declare no conflict of interest.