Methods and systems are provided for data-driven network roll-out planning. A network that includes a plurality of cells may be managed, with the managing includes obtaining network data associated with the network; analyzing the network data, with the analyzing including analyzing throughput of users on a sector-by-sector basis in the network; applying, based on the analyzing of the network data, a growth prediction for the network; and optimizing, based on the applying of the growth prediction, a network roll-out plan for use in the network.

Methods and systems are provided for data-driven network roll-out planning. A network that includes a plurality of cells may be managed, with the managing includes obtaining network data associated with the network; analyzing the network data, with the analyzing including analyzing throughput of users on a sector-by-sector basis in the network; applying, based on the analyzing of the network data, a growth prediction for the network; and optimizing, based on the applying of the growth prediction, a network roll-out plan for use in the network.

The present technology is directed to visualizing a Wi-Fi signal propagation in 3-D at various heights and locations. The present technology can calculate a radio frequency (RF) propagation pattern for a Wi-Fi access point (AP) based on a RF propagation model for the Wi-Fi AP and overlay the RF propagation pattern for the Wi-Fi AP over a visualization of the building plan to present a 3-D visualization of the RF propagation pattern of the 3-D space. In particular, the present technology can project a plurality of ray-paths in various directions in a 3-D space originated from the Wi-Fi AP and determine whether the ray-paths interface with objects defined in the building plan. The present technology can segment the respective ray-path into contiguous segments of substantially uniform mediums for each ray-path that interface with the objects and determine a RF signal strength at points along the contiguous segments of the ray-paths.

The present technology is directed to visualizing a Wi-Fi signal propagation in 3-D at various heights and locations. The present technology can calculate a radio frequency (RF) propagation pattern for a Wi-Fi access point (AP) based on a RF propagation model for the Wi-Fi AP and overlay the RF propagation pattern for the Wi-Fi AP over a visualization of the building plan to present a 3-D visualization of the RF propagation pattern of the 3-D space. In particular, the present technology can project a plurality of ray-paths in various directions in a 3-D space originated from the Wi-Fi AP and determine whether the ray-paths interface with objects defined in the building plan. The present technology can segment the respective ray-path into contiguous segments of substantially uniform mediums for each ray-path that interface with the objects and determine a RF signal strength at points along the contiguous segments of the ray-paths.

The present technology is directed to providing a 3-D visualization of a Wi-Fi signal propagation pattern based on telemetry data. The present technology can receive telemetry data for a Wi-Fi access point located at a location of a building plan in a Wi-Fi visualization system, store the telemetry data with a timestamp, determine a change in a Wi-Fi coverage for the Wi-Fi access point based on the telemetry data, and present a visualization illustrating the change in the Wi-Fi coverage for the Wi-Fi access point. The present technology can further present an animation of the change in the Wi-Fi coverage for the Wi-Fi access point based on the stored telemetry data.

A device, comprising a packet data interface port; a microcontroller, configured to control the packet data interface port, receive a input control signal through the packet data interface port, transmit a status report through the packet data interface port, and in dependence on the input control signal, produce an output control signal; and a radio frequency modification device, configured to modify a received radio frequency signal over a range selectively in dependence on the output control signal. A control processor, communicating through the packet data interface port with the microcontroller, may generate a plurality of the input control signals for a plurality of respective devices comprising the microcontroller and the radio frequency signal control device. The input control signals may be dynamically changed over time to emulate radio frequency conditions resulting from mobility of nodes in a mobile ad hoc radio frequency communication network.

A device, comprising a packet data interface port; a microcontroller, configured to control the packet data interface port, receive a input control signal through the packet data interface port, transmit a status report through the packet data interface port, and in dependence on the input control signal, produce an output control signal; and a radio frequency modification device, configured to modify a received radio frequency signal over a range selectively in dependence on the output control signal. A control processor, communicating through the packet data interface port with the microcontroller, may generate a plurality of the input control signals for a plurality of respective devices comprising the microcontroller and the radio frequency signal control device. The input control signals may be dynamically changed over time to emulate radio frequency conditions resulting from mobility of nodes in a mobile ad hoc radio frequency communication network.

A system described herein may provide for the use of artificial intelligence/machine learning (“AI/ML”) techniques to generate models for various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network. The system may determine Key Performance Indicators (“KPIs”) or other attributes that are of particular relevance or importance for a given sector model, and may determine actions to perform with respect to particular sectors in order to enhance performance according to the KPIs that are of particular relevance to a sector model determined with respect to the particular sectors.

Methods, systems, and devices for wireless communication are described. A network node may determine a first time location associated with a peak in data traffic for multiple devices in communication with a communications network including the network node. The network node may determine a second time location for the peak in the data traffic for a subset of the devices based on a threshold for an overall peak in data traffic for the multiple devices. The network node may transmit a signal that indicates the second time location for the peak in the data traffic for the subset of devices. The network node may communicate the data with the subset of devices based on the signal indicating the second time location.

Methods, systems, and devices for wireless communication are described. A network node may determine a first time location associated with a peak in data traffic for multiple devices in communication with a communications network including the network node. The network node may determine a second time location for the peak in the data traffic for a subset of the devices based on a threshold for an overall peak in data traffic for the multiple devices. The network node may transmit a signal that indicates the second time location for the peak in the data traffic for the subset of devices. The network node may communicate the data with the subset of devices based on the signal indicating the second time location.