# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 5 Results Analysis
### Results of the Channel Based on CSK/QAM Mapping  
The communications channel is the source of all negative effects when collecting data for the purpose of locating and implementing the optimal model. Traditionally, IEEE 802.15.7 specifies rules for designing M-CSK constellations and directly applying them for modulation. However, the mapping defined in Section 4.1 can also be used to indicate which cursor to map. Then, use a square constellation with better separation properties can be used. Figures 8 and 9 illustrate the experimental results for \(N=2^{2^{i}}\times 2^{2^{i}},i=0,1\). Despite their two-dimensional representation in the CIE xyY plane, the null element points can be aggregated with an integer cursor can be assigned to each of them, and a table can be created in the cloud to provide copies at the transmitter and receiver for calculating inverse mapping between the M-CSK and the M-QAM constellation.  
Figure 10 shows the MIMO channel capacity for \(N=2^{2^{i}}\times 2^{2^{i}}(i=0,1,2,3)\). In order to get a greater spectrum efficiency for a longer chromosome, it has been demonstrated that square constellations should be favoured due to their superior separation properties, despite their exponentially increasing complexity.  
Due to the fact that quantum objects do not require their attributes to be specified, a beam of baseband light can arrive at the photodetector from a variety of angles other than a coherent straight line. The use of a laser decreases costs because white light amplification equipment is avoided due to the high concentration of energy in a single frequency tone.  
In order to generalise the FSK channel to a MIMO channel [51], Figures 11 and 12 show the output of the MIMO demodulator that will enter the \"Galois Field demapping\" process for 256-point QAM square constellation with SNR values of \(SNR=60[dB]\) and \(SNR=30[dB]\).  
Figure 13 compares CSK/QAM mapping to QAM mapping by using the XGBoost algorithm in terms of the BER for different SNR values with \(N=2^{2i}\times 2^{2i},i=0,1,2,3\). This simulation was carried out in \(1[dB]\) steps for values up to \(SNR=80[dB]\). The BER was computed within one cycle using the MMSE estimator.This procedure was repeated 10,000 times to accumulate the erroneous values for each of the \(SNR[dB]\) levels in an array. In contrast to linear mapping, the combination of M-CSK and M-QAM mapping results in nonlinear productions, i.e., it breaks the regularity of selecting the same points due to the centroid calculation and the inclusion of points as the null element. As mentioned in Section 4.3.5, the XGBoost algorithm produces the best results because it manages to generate multiple trees and is the only one capable of learning a nonlinear dataset, because it generates a new objective function. When the dataset is small, it cannot learn to predict the values to come, for example, when \(N=2^{2i}\times 2^{2i},andi=0,1\) points occur, although it is seen to start working for \(N=2^{2i},i=3\), but when the dataset increases, the gain is significantly improved. For BER=\(10^{-3}\), gains of \(-10\) [dB], \(-3\) [dB], \(3\) [dB] and \(5\) [dB] occur for \(N=2^{2i}\times 2^{2i},i=0,1,2,3\), respectively. It is concluded that the square constellation \(N=2^{2i}\times 2^{2i},i=3\) produces a greater benefit. When the data set is small, the algorithm is unable to learn to forecast future values. Alternatively, the BER improves as the data set is enlarged, making it simpler to separate states and creating a larger forest.