Characterization of Path Loss and Large-Scale Fading for Rapid Intervention Team Communication in Underground Parking Garages

Abstract

This paper reports the characterization of the 2.45-GHz-ISM-band radio wave propagation channel. Specifically, measurements were performed in an underground parking garage, with the aim of optimizing breadcrumb systems for a Rapid Intervention Team application. The effects of the high penetration loss and large reflections by the concrete reinforced building structure on the path loss and the large-scale fading were studied. Based on the analysis of the wireless channel, critical points for reliable communication between members of a Rapid Intervention Team were identified. In particular, attention was paid to dealing with large, spatially confined signal losses due to shadowing, the anticipation of corner losses and the ability of the system to operate on multiple floors.

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

“…Since radio receivers require a certain minimum power (sensitivity) to be able to successfully decode information, path loss prediction is essential in mobile communications network design and planning such as link budget, coverage analysis, and locating base station. Many existing path loss models [1][2][3][4][5][6][7][8][9][10][11][12][13][14] adopt a linear log-distance model, which is empirically derived by assuming a linear proportionality between the path length and the path loss, and by determining a proportional factor through the adequate linear regression analysis of the measured data. The linear log-distance model is simple and tractable, but it does not guarantee accurate path loss prediction performance for all radio propagation environments.…”

“…The shadow fading denoted by X σ is a Gaussian random variable with zero mean and σ standard deviation in dB. Most measurement based path loss models extend the baseline given in Equation 1to incorporate the effect of other main radio parameters such as frequency [1,3,4,6], the height of the transmit and receive antennas [1,3,4,6], clutter and terrain [1,5,6], the percentage of the area covered by buildings in the built up area [7], and line-of-sight and non-line-of-sight [8][9][10]. Meanwhile, Jo et al [14] improved the Modified Hata model to suit the higher frequency band of 3-6 GHz.…”

Deep Learning-Based Link Quality Estimation for RIS-Assisted UAV-Enabled Wireless Communications System

“…However, we use the statistical LoS channel (i.e., the probabilistic LoS channel) [20] and consider a more practical A2G channel model. This model is characterized by large-scale and smallscale fading calculated based on a simulated map considering the presence of buildings as propagation scatterers [21]. The channel between the UAV and GUs during timeslot t can be determined as follows:…”

Path loss modeling based on neural networks and ensemble method for future wireless networks

“…However, the PLE values vary significantly depending on the environment where the wireless system is placed and the communication scenario. The log-distance model is a widely used empirical model for predicting path loss [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] . This model assumes a linear relationship between path loss and the distance between the transmitter and receiver on a logarithmic scale.…”

“…To achieve this, some researchers have proposed improvements to the log-distance model, such as incorporating the impact of shadowing and other factors like antenna height, operating frequency, clutter, terrain, communication scenario category (i.e., line-of-sight (LOS) or non-line-of-sight (NLOS), etc. ), and the percentage of built-up areas covered by buildings [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] .…”