# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 4 MI-Assisted Adaptive Modulation
### Atmospheric Imperfections  
It is evident that atmospheric imperfections play a vital role in Air-to-Ground communication [32; 34; 35; 36]. Rain, gaseous absorption and scintillation are the main players that contribute to atmospheric imperfection. As we use high frequency for communication, the wavelength of the signal is comparable to the raindrop and hence contributes to large-scale fading [32]. We usually neglect this in terrestrial communication and use smaller frequencies (<5 GHz). Attenuation is also dependent on the frequency of communication, rainfall rate, and distance over which rain is present. An empirical model has been suggested in [37], where the attenuation is modeled by frequency of communication (\(f\)) and rainfall rate (\(R\)). The mathematical model for calculating attenuation per Km is  
\[\gamma=a.R^{b} \tag{7}\]  
where \(a\) and \(b\) are parameters obtained from the table given by ITU for particular operating frequency [37]. The path length (\(r_{R}\)) is to be multiplied to obtain the total rain attenuation to design reliable drone communication [33]. The path length (\(r_{R}\)) is found by  
\[r_{R}=\frac{h_{R}-h_{S}}{sin\theta} \tag{8}\]  
where effective rain height \(h_{R}\) is 5 Km for the northern and southern hemisphere, \(h_{S}\) is the ground station antenna height in meters and \(\theta\) is the elevation angle in degrees. With the path length \(r_{R}\), rain attenuation \(\gamma\) and with an assumption that rain fall is in the vertical direction, the path loss due to rain is found as  
\[L=\gamma\times r_{R} \tag{9}\]  
It is evident that rain in the communication path inversely affects the performance in two ways [42],  
1. The channel is attenuated heavily, and the received signal strength becomes weaker than the desired threshold,
2. An ill-modeled \(\mathbf{H}\) matrix destroys beam-forming vector design. Adaptive modulation has turned out to be an effective method to provide a higher rate by delivering a satisfactory performance. Hence, we propose adaptive modulation as a capacity maximization scheme under atmospheric imperfections. Adaptive Modulation Using K-Means Algorithm  
AM assures satisfactory performance by maintaining the BER threshold, while spectral efficiency is compromised due to the received SNR. In this way, we can switch the modulation schemes as the attenuation due to rain affecting the system. This paper considers BPSK, QPSK, 16-QAM, and 256-QAM as different modulation schemes for adaptive modulation. The proposed algorithm to adapt to the optimal modulation scheme based on varying rain rates is as follows. Algorithm 2 describes the proposed algorithm for adaptive modulation using k-means clustering. Several data points representing BER-SNR are generated by mapping the BER-SNR curve to a linear one, as shown in Figure 2. The BER-SNR curve is generatedusing a semilog plot, whereas the points given for the k-means algorithm are in a linear scale. Cluster heads are determined for data points with the same rain rate. Further, perpendicular distances \(d_{i}\), \(i\in\{1,2,..,K\}\), where \(K\) is the number of modulations used concerning the BER threshold line, are observed. The selection of optimized modulation will be based on the distance between the cluster head and the BER threshold. The cluster head with minimum distance is selected to provide optimal performance. That is, we select modulation with a minimum \(d_{i}\).