## 3 Algorithms
### Unsupervised Learning  
Speaking of the unsupervised learning, autoencoder is probably the most popular deep learning architecture in this category. In general, an autoencoder includes an input layer, several hidden layers, and an output layer. The model aims to adjust its parameters in such a way that the output layer is equal to the input one despite the information bottleneck caused by the hidden layers. The role of the loss function is often played by the reconstruction error which is the difference between the input and the output. There are several autoencoder variations depending on the properties of the hidden layers and additional terms in the loss function. For example, in variational autoencoders (VAEs) the encoder returns a distribution over the latent space instead of a single point. Another autoencoder architecture variation is called sparse autoencoder. To train this architecture, the loss function includes not only the reconstruction error but also a special penalty function. This penalty is the bigger the greater the number of units which fire in the bottleneck layer. Autoencoders can be employed for anomaly detection. This process \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Algorithm** & **Mobile Network Applications** \\ \hline \multirow{8}{*}{\begin{tabular}{c} Fully-connected neural network \\ \end{tabular} } & Channel estimation [28; 57], symbol detection [58], automatic modulation \\  & classification [59], channel coding [60; 61; 62], beamforming [34; 63; 64; 65], \\  & activation control [66; 67], power allocation [68; 37; 69; 70; 71], scheduling [72], \\  & routing [73], security [41; 43; 47; 74] \\ \hline \multirow{8}{*}{\begin{tabular}{c} Supervised \\ \end{tabular} } & Channel estimation [75; 76; 77; 78; 79], automatic modulation \\  & classification [78; 78], channel coding [62; 79; 80; 81; 61], beamforming [34], \\  & power allocation [82], routing [83], localization [84] \\ \cline{2-3}  & \multirow{2}{*}{RNN (GRU, LSTM and ECN)} & Automatic modulation classification [32], channel coding [61], power allocation [85], scheduling [39], routing [83], localization [84], \\  & & security [45], slicing [86], caching [87] \\ \cline{2-3}  & Attention (transformer) & Channel coding [62; 79] \\ \cline{2-3}  & Residual neural network & Beamforming [34] \\ \cline{2-3}  & K-NN & Security [74] \\ \cline{2-3}  & SVM (and SVR) & Beamforming [88], security [47] \\ \cline{2-3}  & Linear regression & Beamforming [88], security [74] \\ \cline{2-3}  & Decision trees (including random \\  & forest and gradient boosting trees) & Beamforming [88], security [74] \\ \cline{2-3}  & Naive Bayes & Security [74] \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} Unsupervised \\ \end{tabular} } & Autoencoder & Channel estimation [77], channel coding [79], scheduling [39], \\  & caching [89], security [43] \\ \cline{2-3}  & GAN & Channel estimation [76], beamforming [90] \\ \cline{2-3}  & Denoising CNN (DIP, LDAMP and \\  & DrCNN) & Channel estimation [91; 92] \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Reinforcement \\ \end{tabular} } & Q-learning & Channel coding [60], beamforming [90], power allocation [93], \\  & & caching [94] \\ \cline{2-3}  & DQN & Activation control [67], power allocation [70; 82; 85], scheduling [95], \\ \cline{2-3}  & DQN & routing [83], slicing [96; 97; 98; 96] \\ \cline{2-3}  & DDPG & Activation control [66], scheduling [72], routing [99] \\ \cline{2-3}  & SARSA & Activation control [36] \\ \cline{2-3}  & Policy gradients & Channel coding [62], scheduling [39; 95] \\ \cline{2-3}  & TRPO & Routing [73] \\ \cline{2-3}  & PPO & Slicing [100] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AI/ML algorithms used by researchers in the mobile networking domain. usually involves two main steps. First, the training data is fed to an autoencoder until it is well trained to reconstruct the expected output with minimum error. Second, the same data is fed again to the trained autoencoder and the error term of each reconstructed data point is measured.