Provided is a coverage indicator prediction method. The method includes: obtaining a wireless cell feature of a wireless cell to be predicted, a geographical environment feature of the wireless cell to be predicted, a grid geographical environment feature, and a feature of a wireless propagation path from the wireless cell to be predicted to a corresponding grid, where grids are obtained by dividing a designated region; and predicting, according to the wireless cell feature of the wireless cell to be predicted, the geographical environment feature of the wireless cell to be predicted, the grid geographical environment feature, and the feature of the wireless propagation path from the wireless cell to be predicted to the corresponding grid, a coverage indicator value of the grids using a trained coverage indicator prediction model. Coverage indicator prediction apparatus, model training method and apparatus, electronic device, and computer-readable storage medium are also provided.

Provided is a coverage indicator prediction method. The method includes: obtaining a wireless cell feature of a wireless cell to be predicted, a geographical environment feature of the wireless cell to be predicted, a grid geographical environment feature, and a feature of a wireless propagation path from the wireless cell to be predicted to a corresponding grid, where grids are obtained by dividing a designated region; and predicting, according to the wireless cell feature of the wireless cell to be predicted, the geographical environment feature of the wireless cell to be predicted, the grid geographical environment feature, and the feature of the wireless propagation path from the wireless cell to be predicted to the corresponding grid, a coverage indicator value of the grids using a trained coverage indicator prediction model. Coverage indicator prediction apparatus, model training method and apparatus, electronic device, and computer-readable storage medium are also provided.

An object of the present disclosure is to provide a server device capable of processing information related to a plurality of areas without mixing the information. The server device according to the present disclosure includes: a communication unit (11) that receives, from a plurality of sensor devices that have collected a packet being transmitted from a first wireless terminal, sensor information in which area identification information for identifying an area in which the sensor device is installed is assigned to wireless quality information determined based on the packet, the wireless quality information indicating wireless quality, in a predetermined period, of the area; a selection unit (12) that selects first area identification information from a plurality of pieces of area identification information, based on the wireless quality information, when a received plurality of pieces of the sensor information include a plurality of pieces of the area identification information; and a display unit (13) that displays information related to the wireless quality information in the area indicated by the first area identification information.

An object of the present disclosure is to provide a server device capable of processing information related to a plurality of areas without mixing the information. The server device according to the present disclosure includes: a communication unit (11) that receives, from a plurality of sensor devices that have collected a packet being transmitted from a first wireless terminal, sensor information in which area identification information for identifying an area in which the sensor device is installed is assigned to wireless quality information determined based on the packet, the wireless quality information indicating wireless quality, in a predetermined period, of the area; a selection unit (12) that selects first area identification information from a plurality of pieces of area identification information, based on the wireless quality information, when a received plurality of pieces of the sensor information include a plurality of pieces of the area identification information; and a display unit (13) that displays information related to the wireless quality information in the area indicated by the first area identification information.

A multi-layer network planning system can determine a set of regenerator sites (“RSs”) that have been found to cover all paths among a set of nodes of an optical layer of a multi-layer network and can determine a set of candidate RSs in the optical layer for use by the links between a set of nodes of an upper layer, wherein each RS can be selected as a candidate RS for the links. The system can determine a binary path matrix for the links between the set of nodes of the upper layer. The system can determine a min-cost matrix that includes a plurality of min-cost paths. The system can determine a best RS from the set of candidate RSs and can move the best RS from the set of candidate RSs into the set of RSs for the links. The system can then update the binary path matrix.

A multi-layer network planning system can determine a set of regenerator sites (“RSs”) that have been found to cover all paths among a set of nodes of an optical layer of a multi-layer network and can determine a set of candidate RSs in the optical layer for use by the links between a set of nodes of an upper layer, wherein each RS can be selected as a candidate RS for the links. The system can determine a binary path matrix for the links between the set of nodes of the upper layer. The system can determine a min-cost matrix that includes a plurality of min-cost paths. The system can determine a best RS from the set of candidate RSs and can move the best RS from the set of candidate RSs into the set of RSs for the links. The system can then update the binary path matrix.

A system for an automated ML-based design of a wireless network. The system includes a processor of a design server node connected to at least one local, edge, or cloud server node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire aerial 3-D mapping data of a target area from an unmanned aircraft system (UAS) flying over the target area; acquire surface 3-D mapping data from a ground robotic crawler; parse the 3-D mapping data to derive an at least one feature vector; provide the at least one feature vector to a machine learning (ML) module residing on the at least one local, edge, or cloud server node for generating a predictive model of a wireless network for some or all of the target area; receive outputs of the predictive model; and generate a wireless network design for the some or all of the target area based on the predictive outputs.

A system for an automated ML-based design of a wireless network. The system includes a processor of a design server node connected to at least one local, edge, or cloud server node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire aerial 3-D mapping data of a target area from an unmanned aircraft system (UAS) flying over the target area; acquire surface 3-D mapping data from a ground robotic crawler; parse the 3-D mapping data to derive an at least one feature vector; provide the at least one feature vector to a machine learning (ML) module residing on the at least one local, edge, or cloud server node for generating a predictive model of a wireless network for some or all of the target area; receive outputs of the predictive model; and generate a wireless network design for the some or all of the target area based on the predictive outputs.

A method and a system for identifying radio access network (RAN) coverage at geographic locations includes generating a grid layer of real-time RAN coverage based on a key performance indicator (KPI). Generating a grid layer of predicted RAN coverage. Generating a viewport representation corresponding to a geographic location supported by the RAN. Superimposing the real-time RAN coverage grid layer, the predicted RAN coverage grid layer, and the viewport representation into a unified coverage representation. Determining RAN availability at a selected geographic location based on the unified coverage representation.

A cell range determination is made by determining a sector straddling an azimuth line of a first base station with a center at the coordinates of a first base station. The sector is divided into a plurality of subsectors. A nearest neighbor base station is determined in each of the plurality of subsectors. A set of coordinates is determined for the nearest neighbor base station. An average distance between the nearest neighbor base stations is determined. A bearing angle difference between the nearest neighbor base station and the first base station is determined based on the set of coordinates of the nearest neighbor base station. A gain is determined for each of the plurality of subsectors based on the bearing angle difference. A cell range is determined for the first base station based on the gain.