# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 3 Data Preparation and Models Setup
### Data Preparation  
The adopted real measured data were collected in a typical indoor corridor on the fifth floor of the discipline of Electrical, Electronic, and Computer Engineering, University of KwaZulu-Natal, Howard College Campus, Durban 4001, South Africa. It is worth mentioning that we ensured that the overall measurement system was carefully calibrated, and there were no other signal transmissions in the same frequency range that were selected for this research before beginning the measurement campaigns. Moreover, to provide a high level of confidence and ensure high-quality data collection from our measurement results, the measurements were repeated and averaged for all the frequency bands and communication scenarios adopted for this paper. As mentioned above, the wireless propagation channel selected for this research study is an indoor corridor environment that is vital and ordinarily used for many wireless indoor applications. These corridors can be analytically approximated as rectangular air-filled waveguides with dimensions immense compared to the signals' wavelengths [10]. The dimensions of our wireless propagation channel (i.e., the indoor corridor) are 30, 1.4, and 2.63 m as the length, width, and height, respectively. The materials used to construct this corridor are bricks and dry concrete. This corridor has wooden doors to offices on one side and an elevator and a staircase on the other side [10]. The measurements were carried out for both the LOS and NLOS communication scenarios at three frequency bands from the SHF band, namely 14, 18, and 22 GHz. Furthermore, two practical heights of the transmitting antenna were selected, 1.6 and 2.3 m. The receiving antenna height was fixed at 1.6 m. The main reason for choosing the Tx antenna height to be mainly these two values (1.6 and 2.3 m above the floor level) is the fact that 1.6 m is approximately the typical user height, while 2.3 m is the height of access points in indoor environments. Moreover, these two values are the average antenna heights for these indoor environments that are adopted by many researchers [61]. The antennas used in this work are vertically polarized horn antennas with directional radiation patterns. Throughout the campaigns, the intent was to place the Tx at one end of the corridor and move the Rx away from the Tx, having a Tx-Rx separation distance of 2-24 m with an incremental step of 2 m a time [10]. In addition, the receiver was mechanically rotated in the azimuth plane to provide several angles of arrival (AoAs), which is the practical case in using the mobile as a receiver for wireless systems. More details about the measurement campaigns, channel sounder, and the environment can be found in [10]. The total number of samples collected from the measurement campaigns is 865 samples, considering all the operating frequencies (i.e., 14, 18, and 22 GHz), Tx-Rx separation distances (2-24 m with an incremental step of 2 m), antenna height values (i.e., 1.6 and 2.3 m), and AoA values (0-360 degrees with an incremental step of 10 degrees). The raw datasets collected from the measurements were analyzed and cleaned to provide one reliable path loss value for each Tx-Rx separation distance, frequency, AoA, and Tx antenna height. Accordingly, the best features that lead to the optimum performance for the ML-based models adopted in this work are: (1) The distance between the transmitting and receiving antennas, which is the most crucial input feature that significantly affects the path loss values. (2) The operating frequency to provide multi-frequency path loss prediction models for the frequency range between 14 and 22 GHz. (3) The AoA of the Rx antenna to has LOS and NLOS characterizations of the communication's condition. (4) The Tx antenna height that allows for more generalization of the target models. The datasets were cleaned since this step is a vital part of any aspect in modeling based on ML algorithms. Working with impure datasets can lead to several significant challenges.