# Assessment of Deep Learning Methodology for Self-Organizing 5G Networks
## 4 Enabling 5G with SON and Deep Learning
### Deep Learning-Based Anomaly Detection
#### 4.2.3 Dataset 1  
In this section, we describe the use of one of the datasets to perform cell outage detection and base station identification given the KPIs obtained by a SON simulator. The simulation was run for 25 steps where each step was 2 s long. There were 7 base stations in total, placed at fixed locations and separated from each other with fixed distances. Each base station consisted of 3 cells, so, in total, 21 cells were simulated. There was a total of 105 users and they moved in the enclosed area with a random walk model. The data generated by the cell consisted of the measurement time, the unique ID of the user, the location co-ordinates of the user in the 2-dimensional plane, the reference signal received power, the reference signal received quality, and the label indicating whether the user was anomalous or non-anomalous. The KPI selected for the use case was reference signal received power (RSRP) and reference signal received quality (RSRQ) which are the key measures of signal level and quality for LTE networks. When a mobile user moves from one cell to other cells and performs cell selection/reselection and handover, it has to measure the signal strength and signal quality of the neighbor cells. The RSRP is the average received power of a single reference signal of resource element whereas RSRQ is the average received quality of the reference signal of resource element. RSRP measurements are used to perform user equipment (UE) Attach, cell selection, cell re-selection, handover procedures. The RSRQ measurements provide additional information when RSRP is not sufficient to make a reliable handover or cell reselection decision. Cell outage is the state of the cell when a cell is not able to serve the users due to the network performance degradation. The cell outage is caused by hardware and software failures (radio board failure, channel processing implementation error, etc.), external failures such as power supply or network connectivity failures, or even erroneous configuration. It is important to detect the cell outages as quickly as possible to recover the coverage loss and minimize the revenue loss caused by cell outages. The users effected from cell outages are termed as 'anomalous users'. The anomalous user detection is crucial step to detect the cell outages. The feature vector was \(\{\)RSRP\({}^{1}\), RSRQ\({}^{1}\), RSRP\({}^{2}\), RSRQ\({}^{2}\), \(\dots\), RSRP\({}^{9}\), RSRQ\({}^{9}\), RSRP\({}^{10}\), RSRQ\({}^{10}\)\(\}\). It was clear that the units of measurement were quite different, so pre-processing was required, as described in the previous section. The simulation was manually handled from the User Interface (UI),and when a cell was broken, its transmission power from the base station was set to 0 dbm. This was not necessarily reflected in the RSRP and RSRQ values. The KPIs were preprocessed to handle missing values and then scaled between 0 and 1.  
We split the KPIs into 80% for training and 20% for testing after the rows had been randomly shuffled. The training set only consisted of normal users, while both normal and abnormal users were part of the test set. During training, the test set was used for validation as well. Since the input vector size was 20, the first encoding layer of the autoencoder model had a size of 20, and the output layer size was the same. The autoencoder model structure was 20-12-6-6-12-20. This structure was chosen with the idea that the hidden layers would be able to compress the input information and avoid identity mapping from inputs to outputs.