## 1 Introduction  
Fifth Generation (5G) cellular networks are promising to be a real improvement compared to all the previous mobile generation networks [1]. They bring three main services for end-users i.e., Extreme Mobile Broadband (eMBB), Massive Machine Type Communications (eMTC), and Ultra-Reliable Low Latency Communication (URLLC) [1, 2]. The 5G New Radio (5G-NR) operates over two frequency ranges: Frequency Range 1 (FR1 includes sub-6 GHz), and Frequency Range 2 (FR2, which includes the millimeter-wave band, from 24.25 to 52.6 GHz). A significant number of commercial 5G networks are currently under deployment in the sub-6 GHz band, particularly in the 3.3-3.8 GHz (3.5 GHz band) portion, which offers a good trade-off between coverage and capacity [3]. Therefore, understanding signal propagation characteristics in the 3.5 GHz band is particularly important, especially in 5G indoor scenarios, where many applications are being developed. Some 5G use cases target indoor scenarios, such as smart building monitoring with different types of sensors, factory automation, object tracking inside a building, and automated vehicles (logistic applications) [4, 5]. These indoor scenarios are likely to be addressedvia an outdoor (outdoor-to-indoor, O2I) deployment of the 5G Base Station (BS) [6; 7; 8]. However, it was demonstrated in [9] that the PL for O2I increases by approximately 100 dB when the distance between the BS and the building is increased by 50 m, and the use of indoor-to-indoor (I2I) deployments can reduce the PL up to 60 dB, compared to O2I deployments. This emphasizes the need for understanding propagation characteristics in indoor scenarios with I2I deployments. Wireless network planning and optimization are indispensable in modern wireless communication systems, considering the ever-increasing number of Radio Frequency (RF) devices in our daily lives. This usually requires the evaluation of many candidate solutions to find the best system settings and to meet the Quality of Service (QoS) requirements. This is only feasible for simple Path Loss (PL) models, such as one-slope or multi-wall models. These are easy to construct, but their validity is limited to the propagation environment they represent. Furthermore, the estimation of the propagation PL in indoor scenarios is not trivial, due to the complexity and variabilities of the environment. Other approaches are to calculate PL using deterministic models, for example using ray-tracing [10; 11], or using a heuristic approach [12]. Deterministic propagation path loss models could provide more accurate estimations of the PL, but they are time-consuming. When calculating one candidate network deployment takes too long, it is unlikely that a wireless network planning algorithm will converge to an optimal solution, i.e., providing coverage with a minimal number of access points. Hence, these complex PL models, despite being more accurate, are less suited to use in the optimal network design process. Alternatively, PL estimations using Machine Learning (ML) techniques are efficient, consume little time, and can be adjusted to fit any environment or propagation condition. As such, these models allow the evaluation of a large number of candidate network deployments when performing network planning, which should eventually lead to an optimal network deployment, that, at the same time, builds on an accurate path loss model. In this research work, we propose a generic method based on the Decision Tree Ensembles (Bagging) algorithm for the estimation of the PL experienced by 5G signals at 3.5 GHz in indoor scenarios. Subsequently, the obtained model is used in a Genetic Algorithm (GA) [13] to perform 5G network planning.