## 2 NB-PLC and RF Characterization
### RF Characterization
#### 2.2.1 RF Attenuation  
In this paper, the authors are interested in outdoor environments (rural, urban, and industrial) where the wireless signal is subject to several fading phenomena, such as reflection, refraction, diffraction, etc. Channel modeling is one of the common challenges for the Wi-SUN mesh network [38]. This process is primarily employed to predict signal characteristics including channel behaviors such as propagation phenomena (absorption, reflection, scattering, and diffraction), interference sources (involving thermal noise and shared unlicensed frequency bands), and the dynamic nature of the environment (multipath fading channels). By solving Maxwell's equations for all physical elements and environmental factors, the channel model is obtained, acquiring multipath fading channels. Consequently, a pragmatic approach involves focusing the channel model on the primary propagation effects, namely reflection, diffraction, and scattering, while disregarding less pertinent details to streamline complexity. These fading phenomena are categorized into two distinct groups: large-scale fading and small-scale fading. Large-scale fading characterizes the gradual alteration of signals, primarily attributed to the combined effects of path loss and shadowing. Oppositely, small-scale fading pertains to the swift fluctuations of signals on a smaller scale, driven by three fundamental factors: the signal's delay spread, temporal variations within the channel, and the transmission bandwidth of the channel. For smart metering applications, some of the key components and considerations in an RF channel model include path loss and multipath propagation. Path loss, expressed in dB, refers to the attenuation or reduction in the strength of a wireless signal as it propagates through space over a certain distance. It is a critical factor because it directly affects the reliability of wireless communication links between smart meters and data concentrators. Authors in [39] estimated the connectivity of a smart meter and a data concentrator using a more detailed approach. They employed a path loss model, which includes link-specific terrain profile information to compute diffraction losses. This allows for a better estimation of the average link-received power for the complete path between each SM and the DC around it. Smart meters are typically deployed across various environments, such as rural, urban, and industrial areas. Hence, path loss can vary significantly based on factors such as distance, operation frequencies, and obstacles. For outdoor environments, its typical values vary from 2.7 to 6.5 depending on the environment characteristics [40]. In [41; 42], research studies have demonstrated that the path loss exponent is typically observed to be around 4 for urban areas. In industrial areas, the path loss exponent tends to be around 3, indicating a slightly lower rate of signal attenuation compared to urban areas. On the other hand, flat-lying rural areas generally exhibit even lower values of the path loss exponent. These variations in the path loss exponent reflect the different characteristics of each environment and the impact of obstacles, terrains, and other factors on signal propagation. The multipath propagation occurs when signals take multiple paths between the SM and the DC, leading to constructive or destructive interferences. Interference emerges from the fusion of signals arriving from diverse directions--a direct path, reflections, diffractions, and scatterings--each accompanied by random amplitudes, phases, and delays. This causes fading and impacts signal strength and the SNR or BER. Therefore, the complex nature of wireless communication channel behavior requires well-constructed channel models for an accurate analysis and network optimization. The Rayleigh fading model is adopted, and it shows the effects of small-scale fading of a non-line-of-sight channel in wireless communications.