## 1 Introduction  
The massive deployment of fifth-generation (5G) cellular networks and their integration with the deployment of the Internet of Things (IoT) has led to a significant increase in the amount of wireless traffic. This is due to the potential of IoT-focused applications such as augmented reality, autonomous driving, telemedicine, and high-definition video. This creates a need for a broadband spectrum with the capacity to handle this huge traffic demand. To provide additional spectrum for 5G services, the Federal Communications Commission (FCC) has proposed the low-frequency bands below 1 GHz, the mid-frequency bands from 1 GHz to 6 GHz, and the high-frequency bands, mmWave [1]. To be sure, 5 GAmericas [2] is a review of 5G spectrum considerations for the Americas, Europe, East Asia, and Australia. The review discusses the characteristics, challenges, opportunities, and identification of the potential of the low, medium, and high-frequency bands for the 5G services. The International Telecommunication Union (ITU), through the ITU-R M.2150-1 recommendation defines the radio interface specifications for the terrestrial component of the International Mobile Technologies 2020 (IMT-2020). The ITU details the characteristics and parameters to achieve global compatibility, international roaming, and access to services in the use environments: Indoor Hotspot (InH)--enhanced Mobile Broadband (eMBB); Dense Urban (DU)--eMBB; Rural (R)--eMBB; Urban Macrocell (UMa)--Ultra-reliable Low-latency Communication (URLLC), and UMa--massive Machine Type Communication (mMTC) [3]. A university campus IoT scenario consists of applications that create a smart environment, such as a smart office, smart transport, smart research laboratory, smart campus corridor, smart sports, and smart library; see Figure 1. These spaces allow access to all services with less effort and time. This smart system will need to interact with the environment around it. It therefore requires an automated 5G infrastructure where all devices and sensors are connected. One of the main challenges in deploying this infrastructure is to carry out propagation channel measurement campaigns to generate path loss models that predict propagation loss behavior for the optimal deployment of device and sensor networks in 5G-IoT ecosystems. Likewise, for the integration of technologies such as Low-power Wide Area Networks (LPWAN) and Low-power Wireless Sensor Networks (LPWSN) [4]. As a result, the goals set for 5G networks such as large bandwidths, higher data rates, massive connectivity, low end-to-end latency, cost-effectiveness, consistent quality of service, device computational capabilities, and device intelligent services [5] will be achieved. Some groups of researchers have been proposing path loss models in the mmWave band for 5G networks. In [6], the authors compile and compare the path loss models for UMa, Urban Microcell (UMi), InH mixed, and open office in Line-of-Sight (LOS) and Non  
Figure 1: University campus IoT based on smart applications. LOS (NLOS) propagation conditions scenarios, which were developed by some groups and organizations. In [7], the path loss results obtained experimentally in an office environment in the 25-40 GHz frequency band under LOS and Obstructed LOS (OLOS) conditions are considered. Similarly, some researchers have performed propagation channel measurements to analyze path loss models in corridor environments [8; 9; 10; 11]. However, very few studies have focused their efforts on generating path loss models in the medium and low-frequency bands of 5G for corridor environments [12; 13]. In this study, an extensive measurement campaign was carried out in a covered corridor environment to obtain the received signal power levels. This data set has allowed the estimation of path loss exponent (PLE) values for the close-in free space reference distance (CI) and the floating-intercept (FI) path loss prediction models at 850 MHz and 3.5 GHz.