## 2 Big Data in 5G  
Based on its source, the mobile network data can be divided into two main categories: network-level and application-level [22]. The former is obtained throughout network infrastructure while the latter as a rule is collected by the mobile devices on the edge.  
Network-level mobile data may include global mobile network performance metrics which include but are not limited to throughput, end-to-end delay, and jitter, as well as individual session times, information about the sender and the receiver, communication types, call detail records (CDRs) and many others. Network-level data is usually utilised for network diagnosis and management. Moreover, spatio-temporal patterns extracted from the network-level data can be employed for user mobility analysis and public transportation planning. Network-level data can be further subdivided into the following four sub-classes: radio information, CDR, key performance indicators (KPI), and infrastructure data [8]. The first group includes such radio signal statistics as the serving BS, modulation scheme, spectrum, frequency and signal power. CDR data may contain information about the sender and the receiver, packet inter-arrival times, session start and end times and several others. Frequently used KPIs in the third group are bit/packet error rate, end-to-end delay, jitter, quality of experience (QoE), and throughput. The last group consists of information about equipment holders, infrastructure locations and capabilities.  
In distinction from the network-level category, application-level data is recorded by mobile devices' sensors or applications installed on those devices. The data sources may include mobile cameras and video recorders as well as global positioning systems (GPS) and several others. Data gathering, preprocessing, and subsequent distribution of such data to specific locations is carried out on mobile devices themselves [23]. The data as a rule is related to public events and it is collected through crowdsourcing schemes. Such increase of social data can be explained by availability of the mobile Internet which has made multimedia communication accessible for everyone. Furthermore, preferences of individuals or social groups can also be extracted and analysed due to wide spread and popularity of mobile social networks. Another distinct group of application-level data is related to cloud servers and multimedia content stored on them. Users' preferences for specific multimedia content can be analysed and used to predict content demands in the future which in turn allows the network providers to perform recommendation actions in order to improve user experience.  
Many studies devoted to application of AI/ML in wireless networks generate the data using various simulation software. Such software includes specialised network simulators such as Vienna LTE-A [24], 6 TSCH [25] and OMNeT++ [26]. Another group of researchers generate the data implementing well known channel models using programming and scripting software, e.g., Python and Matlab. The third data simulation option is using a real radio network testbed. Even though this option allows one to carry out the most realistic experiments, it is obviously the most expensive one. In addition, such an approach is most of the time not scalable in the lab environment. Finally, the data for training and evaluating AI/ML models can be retrieved from publicly available datasets. The datasets used in the studies surveyed and summarised later in this document are listed in Table 1.