# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.8 Visible Light Communications  
Effective Radio Frequency (RF) communications systems in indoor use-cases emerge as an important challenge in 6G networks. Visible Light Communications (VLC) as a potential technology, can offer various solutions to this issue. VLC is based on the principle of modulating Light Emitted by Diodes (LEDs), without affecting the human eye, giving an opportunity to exploit the existing illumination infrastructure for wireless communication. VLC technology is expected to offer very high data-rate short-range communications, needed for 6G Networks [90]. 6G is expected to support transmission rates 100-1000 times higher than those for 5G, so there will be growing frequency and bandwidth demands.VLC can employ high transmission rates and use unlicensed bands. So, it is a promising technique to replace conventional wireless local area networks for indoor communications in 6G networks [94].  
Optical Wireless Communications (OWCs) will be widely used in 6G networks and among them, VLC is the most promising frequency spectrum because of the technology advancement and extensive using of light-emitting diodes (LEDs). VLC-based communications do not emit electromagnetic (EM) radiation and have minor interference with other potential EM interference source. Furthermore, VLC has significant advantages in terms of communication security and privacy [95].  
VLC can also be widely exploited in Vehicle to everything (V2X) applications and especially in n Vehicle to Vehicle (V2V) applications [90]. In [94], some clustering unsupervised ML techniques (K-means and clustering algorithm perception decision-CAPD) have been proposed to reduce non linearity in VLC systems. In 2017, CAPD was applied in a multi band VLC system, with the results showing an improvement in the Q-factor by 1.6-2.5 dB. Furthermore, in 2018 a K-means-based pre-distorter was proposed, leading to a 50% improvement of performance [94]. The data for the unsupervised ML models used in 6G problems are listed in Table 7.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline
[68; 69] & K-means & Power allocation & Addresses user selection issue and achieves power optimization in NOMA systems \\ \hline
[70] & PC algorithms & D2D systems optimization & Optimizes power control, computational complexity, throughput and resource allocation \\ \hline
[71; 72] & CNN, LSTM & Modulation recognition & Achieves better performance results in modulation recognition \\ \hline
[77] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[78] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[79] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[82] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[80; 81] & LSTM & Channel estimation &
\begin{tabular}{} \end{tabular} \\ \hline
[83] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[84] & HMM & User’s location &
\begin{tabular}{} \end{tabular} \\ \hline
[85] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[86] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[87] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[88] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline
[87] & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Unsupervised ML models in 6G problems.