Large scale survey for radio propagation in developing machine learning model for path losses in communication systems

Optimizing Antenna Positioning for Enhanced Wireless Coverage: A Genetic Algorithm Approach

“…The optimization of antenna positioning plays a crucial role in the efficient design and deployment of wireless networks. Determining radio propagation characteristics for a specific terrain is a key consideration in this process [10], and for this purpose, radio propagation models are useful [25]. The selection and application of these models depend on the radio propagation environment, which can be divided into rural, suburban, and urban areas [10].…”

“…Determining radio propagation characteristics for a specific terrain is a key consideration in this process [10], and for this purpose, radio propagation models are useful [25]. The selection and application of these models depend on the radio propagation environment, which can be divided into rural, suburban, and urban areas [10]. Each environment presents unique characteristics due to various obstacles that affect radio wave propagation, such as reflection, refraction, diffraction, scattering, or blocking [10,25,26].…”

“…The selection and application of these models depend on the radio propagation environment, which can be divided into rural, suburban, and urban areas [10]. Each environment presents unique characteristics due to various obstacles that affect radio wave propagation, such as reflection, refraction, diffraction, scattering, or blocking [10,25,26]. All these elements must be considered in the path loss model, which represents the decrease in signal power as it propagates from the transmitter to the receiver [10,27].…”

“…There are several propagation models that can be deterministic or empirical, and even a combination of both [10,26,28]. Of these, empirical methods are the most commonly used due to their simplicity and applicability to any experimental area [10,[25][26][27]. These empirical models are strictly derived from field measurements and rely on path loss prediction [25,26,29].…”

“…Through a variety of mathematical formulations and models, such as those introduced by Reininger and Calégari et al [8,9], the goal is to achieve optimal coverage of specific geographical areas with the smallest possible number of antennas. These models account for signal path loss, influenced by physical and environmental factors, and the impact of obstacles on signal transmission and reception [5,10].…”