Abstract: A system identifies first areas of a plurality of areas of a wireless network for wireless site installation. The system performs machine learning to associate measured network performance parameters within the wireless network with empirical data related to at least one of population, residential buildings, or business buildings within the identified first areas of the wireless network. The system optimizes, based on the machine learning, geographic placement of prospective wireless sites within each of the identified first areas of the wireless network to maximize improvements in network performance parameters associated with installing the prospective wireless sites. The system provides optimized wireless site placement recommendations for wireless site installation in each of the identified first areas.

Abstract: A system identifies first areas of a plurality of areas of a wireless network for wireless site installation. The system performs machine learning to associate measured network performance parameters within the wireless network with empirical data related to at least one of population, residential buildings, or business buildings within the identified first areas of the wireless network. The system optimizes, based on the machine learning, geographic placement of prospective wireless sites within each of the identified first areas of the wireless network to maximize improvements in network performance parameters associated with installing the prospective wireless sites. The system provides optimized wireless site placement recommendations for wireless site installation in each of the identified first areas.

Abstract: A system identifies first areas of a multiple areas of a wireless network for wireless site installation and determines geographic locations within each of the identified first areas for prospective wireless site installations. The system performs machine learning to associate measured network performance parameters within the wireless network with empirical data related to population, population densities, housing growth rates, housing densities, and building growth rates. The system determines, based on the machine learning, geographic locations within each of the identified first areas for installations of prospective wireless sites, and forecasts improvements in network performance parameters associated with building each of the prospective wireless site installations.

Abstract: A system identifies first areas of a multiple areas of a wireless network for wireless site installation and determines geographic locations within each of the identified first areas for prospective wireless site installations. The system performs machine learning to associate measured network performance parameters within the wireless network with empirical data related to population, population densities, housing growth rates, housing densities, and building growth rates. The system determines, based on the machine learning, geographic locations within each of the identified first areas for installations of prospective wireless sites, and forecasts improvements in network performance parameters associated with building each of the prospective wireless site installations.

Abstract: A system determines an optimized number and placement of wireless stations based on predicted future demand scoring of end user devices. A computing device calculates a growth projection of mobile devices in a geographic region for a time period; calculates a visiting projection for a peak amount of mobile devices connected to wireless stations in the geographic area and their locations when connected to wireless stations within the time period; calculates an application-use projection for bandwidth use patterns of the mobile devices within the time period; and calculates a network impact projection for an amount bandwidth impact by the mobile devices at each of the wireless stations. The computing device generates a demand score for geographic units within the geographic region, based on the projections, and assigns, based on the demand score, one or more of the geographic units as placement locations for new wireless station during the time period.

Abstract: A system determines an optimized number and placement of wireless stations based on predicted future demand scoring of end user devices. A computing device calculates a growth projection of mobile devices in a geographic region for a time period; calculates a visiting projection for a peak amount of mobile devices connected to wireless stations in the geographic area and their locations when connected to wireless stations within the time period; calculates an application-use projection for bandwidth use patterns of the mobile devices within the time period; and calculates a network impact projection for an amount bandwidth impact by the mobile devices at each of the wireless stations. The computing device generates a demand score for geographic units within the geographic region, based on the projections, and assigns, based on the demand score, one or more of the geographic units as placement locations for new wireless station during the time period.