###### Abstract
Impulse noise in PLCs can originate from various sources, including electrical appliances, switching operations in residential and industrial environments, power fluctuations, and external sources such as lightning or radio frequency interference (RFI), among other factors [10; 16; 17]. The operation of appliances such as refrigerators, air conditioners, and dimmer switches can generate impulsive disturbances due to their switching actions or motor operations. Additionally, power line network connections and power supply fluctuations within the household can cause transient impulses that affect the quality and reliability of the PLC signal. Furthermore, external sources of noise, such as nearby lightning strikes [18] or RFI from electronic devices, can couple into the power line network and manifest as impulsive noise, further degrading the performance of in-house PLC systems. Undoubtedly, one of the most important natural sources of impulse noise is lightning (atmospheric discharge). It happens when electricity discharges between clouds or between a cloud and the ground, causing the sudden release of energy that manifests as a bright flash and a loud thunderlap. Comprehending the statistical properties of impulse noise, its effects on data transmission, and methods to mitigate the noise's negative effects on PLC networks is therefore essential. Further, due to the unique noise characteristics of PLC channels, modulation and coding methods designed specifically for Gaussian channels may not be optimal for PLC channels. For these reasons, there has been a rise in interest in the modeling of and research into the statistical characteristics of PLC noise to assist in developing and designing reliable PLC systems. In this study, we examine the impact of additive impulse noise on the data transmission and functionality of an indoor low-voltage (LV) PLC system. A review of multiple noise measurements in PLC environments, in particular, shows that noise in PLC networks is not Gaussian, is correlated, and is very impulsive [7; 14; 19]. The Gaussian distribution, which describes the statistical properties of additive noise in conventional communication systems, is not sufficient to model the impulsive nature of additive impairments on a PLC channel [13]. The Class-A model [20] and the Bernoulli-Gaussian model [21] are commonly employed to characterize impulse noise PLCs. Nevertheless, due to their lack of memory, these mathematical models fail to account for a crucial characteristic of real channels: impulsive burst errors. However, experimental research by [PERSON] et al. [7] has demonstrated that the partitioned Markov model (PMM) provides an accurate statistical model of the noise process in the PLC context. [PERSON] [22] devised the PMM and used it to describe how errors in binary bursty channels depend on statistics. [PERSON] et al. [7] also showed that PMM is an efficient model for accurately simulating additive noise in PLC compared to experimental measurements. This is because PMM can take into account how impulses occur in bursts. Subsequently, the Fritchman-Markov Model (FMM) [22] has been utilized to study the statistical properties of noise and disturbances and also to model noise processes in a wide range of digital communication problems [14; 23; 24]. In [23], [PERSON] and [PERSON] used the Fritchman model and the Baum-Welch algorithm to model the noise and disturbances on the in-house CENELEC A-band channel based on experimental measurements. [PERSON] et al. [25] modeled impulsive noise over an automotive power line communication network with an improved Markov model. [PERSON] et al. [24] also developed different phase-shift keying (PSK) modulation schemes using software-defined radio and the universal software radio peripheral (USRP) as an experimental platform for the modeling of noise and disturbances on the low-voltage in-house CENELEC A-band environment with a first-order Markov model. [PERSON] et al.