## 3 Algorithms  
In this section, we summarise multiple AI/ML algorithms which are employed for big data analysis in 5G networks. They are usually grouped into one of the following three general categories depending on whether or not a human supervision is required: supervised, unsupervised and reinforcement. Supervised learning requires large quantities of human-labelled data available to learn a functional mapping between the input features and the output labels. Two main advantages of supervised learning are the convergence speed and high accuracy. Unsupervised learning infers the underlying information structure of the data without any external supervision. As a rule unsupervised learning methods are less accurate than supervised ones, but on the other hand no prior knowledge is required to train an AI/ML model in an unsupervised way. Finally, reinforcement learning (RL) discovers optimal actions through interactions in uncertain time-varying environments with the help of reward signals retrieved by an agent during the learning process. Direct supervision is not required for an RL agent, but shaping an efficient reward function may require considerable manual effort. It is also worth mentioning that training an RL agent is most of the time carried out in a realistic simulation software designed to emulate the corresponding real world environment since often training in the real world environment is either extremely time consuming and expensive or not feasible at all. Furthermore, implementing such a simulation software may require huge amounts of resources which poses additional limitations on this machine learning approach.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Description** & **Features and Labels** & **Mobile Network Applications** \\ \hline DeepMIMO [27] & Dataset for mmWave and massive MIMO & Channel matrix, ray-tracing path parameters, line-of-sight status, tx-rx distance path loss, UE and BS locations & Channel estimation in MIMO [28] \\ \hline RadioML [29] & Synthetic dataset consisting of several modulations at varying SNRs & The received signal in in-phase/quadrature (I/Q) format, SNR, modulation type & Automatic modulation classification [30, 31, 32] \\ \hline Raymobtime [33] & Realistic dataset with ray tracing, mobility and time evolution & UE positions, ray-tracing, lidar and video image data & Beam selection [34] \\ \hline SPHERE [35] & Activity recognition with multi-modal sensor data & Accelerometer data, features extracted from video, passive sensor data, activity & Activation control [36] \\ \hline Massive MIMO [37] & Data for power allocation in the downlink of a massive MIMO network & UE positions, max-min and max-prod power allocation vectors & Power allocation [37] \\ \hline IBM/Watson [38] & SNMP records for IBM Watson research centre over several weeks & Aggregated network traffic features for each user of several APs & Scheduling [39] \\ \hline CTU [40] & Real botnet, normal and background traffic & Raw packet captures, per-flow network traffic data features, flow labels & Network intrusion detection [41] \\ \hline AWID [42] & Real 802.11 WLAN traces including normal and malicious traffic & MAC layer, radio and general frame information extracted from 802.11 & Network intrusion detection [43] \\ \hline CICIDS18 [44] & Realistic network traffic data for multiple attack scenarios & Raw packet captures, per-flow network traffic data features & Network intrusion detection [45] \\ \hline WSN-DS [46] & Dataset for intrusion detection systems in wireless sensor networks & Node state features: energy consumption, packets sent/received, distance to BS, attack labels & Jamming detection and classification [47] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobile network datasets used by researchers for training AI/ML models.