# Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G
## 1 Introduction
### Related Work  
Several attempts are made in applying ML in achieving AM. Various branches of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) have been applied to achieve AM [20]. In addition to the techniques mentioned above, Deep Learning techniques, which are used consistently along with various ML algorithms, are also used to achieve AM. Table 1 lists related works, and comparisons with existing works are mentioned afterward. Supervised learning, a division of ML, assisted adaptive modulation is proposed in the multiple-input multiple-output (MIMO) system in [21]. In [22], the authors proposed a support vector machine-based algorithm, a supervised learning algorithm, assisted adaptation where the system learns to adapt to the best modulation without any external training. A link adaptation algorithm aiming to minimize computational time is proposed in [23]. The authors used the support vector machine (SVM) technique to achieve the same results. In a similar work [22], the authors used the SVM technique to achieve channel and modulation selection in cognitive radio. In [24], the authors used the K-Nearest Neighbour (K-NN) algorithm to achieve adaptive modulation in the underwater acoustic network. However, in addition to K-NN, they used an unsupervised algorithm as a tool to condense the data set. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Highlighted** & **Advantages** & **Limitation and Future Directions** \\ \hline
[21] & Link adaptation in OFDM using K-NN algorithm & -Supervised learning algorithm, which works well, if training data are available. & Training data are required in supervised learning \\  & -Applied in the MIMO system & -Supervised learning algorithm, training improves performance & Applied in cognitive radio, not generalized. \\ \hline
[22] & Fast link adaption using ML algorithm & -Uses SVM method for fast adaption & Data set is required for training \\  & & -Improved performance verified with practical experiments & k-means is used for training set condensation \\  & & -Uses K-NN and k-means algorithm & \\ \hline
[25] & Adaptive modulation in wired communication where different cable modems with similar channel conditions are clustered & -Classification using k-means improves performance in wired OFDM transmission & Applied in wired channel and extension to wireless is not considered \\ \hline
[26] & Application of K-means clustering in multi-user Massive MIMO scenario & -K-Means clustering is used for clustering user groups and clusters that maximize capacity are selected & Applicable in Massive MIMO scenario \\ \hline
[27] & Novel framework for AM in OFDM & -Used Q-Learning, an RL algorithm & Performance is poor in initial stages. \\ \hline
[28] & Link adaptation in OFDM & -Same as above & RL requires huge time to converge \\  & & -Explore study about RL in AM is carried out & RL requires huge time to converge \\ \hline
[29] & Q-learning-based adaptive modulation for 5G network & -Conputationsally less complex & -Need not possess big storage as RL does not require previous data & RL takes huge time for convergence. \\ \hline
[30] & Deep reinforcement learning-based adaptive modulation in cognitive heterogenous networks & -Can process complex data & -By improving the neural network we can improve the performance and/or capacity of the data processing & Computationally complex and drones have limited computational power. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with the existing works. Unsupervised algorithms were also considered, but not extensively, to achieve AM. The authors of [25] evaluated the k-means algorithm, an unsupervised algorithm, to achieve adaptive modulation in wired channels, where different cable modems with varying channel conditions are clustered. The k-means algorithm was used for user group clustering in [26]. Further, the authors consider the maximization of channel capacity by selecting appropriate clusters.