In recent decades, the widespread use of mobile phones and wireless technologies has led to a significant increase in radiofrequency electromagnetic fields (RF-EMFs), raising concerns about continuous RF-EMF exposure among the general population. Recent research indicates that real-life RF signals are more biologically active than controlled laboratory signals with a low variability, suggesting that living organisms can adapt to EMF exposure when the pattern has a low variability. However, using real-life sources with unpredictable variation signals in biological experiments contradicts the principle of experiment controllability. This paper aims to investigate the nature of signals generated by current sources of ambient EMFs in terms of stationarity, with the goal of replicating them in biological experiments to study the effects of EMF exposure. Employing a range of statistical methodologies, starting with descriptive statistical analysis and progressing to the advanced APDP and APTF methods, an examination is conducted on a collection of Wi-Fi signal recordings across various operating modes, with particular attention given to video streaming. The chosen datasets are scrutinized with respect to their adherence to a Gaussian distribution and the concept of stationarity. The results indicate that the observed Wi-Fi signals lack stationarity in both the time and frequency domains. However, based on the analytical findings, it is possible to generate signals in frequency that authentically replicate Wi-Fi signals, accounting for nonstationarity considerations.