Aspects of the subject disclosure may include, for example, a process for selecting equipment locations such as of cellular antennas, based on a combination of a geospatial grid representation of a planning area and optimization algorithms (which can be combined with propagation models and a 3D model of the world) where the optimization algorithm can select a deployment from a large space of options and would make RAN planning much more efficient. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, a process for selecting equipment locations such as of cellular antennas, based on a combination of a geospatial grid representation of a planning area and optimization algorithms (which can be combined with propagation models and a 3D model of the world) where the optimization algorithm can select a deployment from a large space of options and would make RAN planning much more efficient. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, network deployment or radio-propagation computation based on a combination of photon mapping and machine learning including supporting near-real-time computation of the radio transmissions for different layouts of antennas and allowing examination of a large variety of antenna locations and layouts, changing configuration details, e.g., tilting antennas or optimally selecting the sector that each antenna covers, and so on. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, network deployment or radio-propagation computation based on a combination of photon mapping and machine learning including supporting near-real-time computation of the radio transmissions for different layouts of antennas and allowing examination of a large variety of antenna locations and layouts, changing configuration details, e.g., tilting antennas or optimally selecting the sector that each antenna covers, and so on. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, identifying geographic clusters that are similar based on metrics and geo-spatial association. Embodiments of the disclosure are directed to operations that include obtaining data corresponding to a communication network, applying a first algorithm to the data to generate a plurality of bins, generating a respective score for each bin of the plurality of bins, applying a second algorithm, based on the respective scores, to generate a plurality of clusters, and generating a graph for each cluster of the plurality of clusters, wherein vertices of each graph are represented by the bins of the cluster, and wherein edges of each graph connect the bins of the cluster to adjacent bins of the cluster. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, identifying geographic clusters that are similar based on metrics and geo-spatial association. Embodiments of the disclosure are directed to operations that include obtaining data corresponding to a communication network, applying a first algorithm to the data to generate a plurality of bins, generating a respective score for each bin of the plurality of bins, applying a second algorithm, based on the respective scores, to generate a plurality of clusters, and generating a graph for each cluster of the plurality of clusters, wherein vertices of each graph are represented by the bins of the cluster, and wherein edges of each graph connect the bins of the cluster to adjacent bins of the cluster. Other embodiments are disclosed.

A method to perform operations includes receiving an electromagnetic (EM) loss request requesting an EM loss between a first requested geographical point and a second requested geographical point. For each end geographical point in a set of different end geographical points that share a same source geographical point, the operations include obtaining a respective EM path loss value between the corresponding end geographical point and the source geographical point and generating a set of virtual loss lines. For each respective virtual loss line, the operations include determining a respective virtual EM loss based on the respective EM path loss values obtained for each end geographical point in the respective pair of end geographical points connected by the respective virtual loss line. The operations include estimating the EM loss between the first and second requested geographical points based on the virtual EM losses determined for the set of virtual loss lines.

A method to perform operations includes receiving an electromagnetic (EM) loss request requesting an EM loss between a first requested geographical point and a second requested geographical point. For each end geographical point in a set of different end geographical points that share a same source geographical point, the operations include obtaining a respective EM path loss value between the corresponding end geographical point and the source geographical point and generating a set of virtual loss lines. For each respective virtual loss line, the operations include determining a respective virtual EM loss based on the respective EM path loss values obtained for each end geographical point in the respective pair of end geographical points connected by the respective virtual loss line. The operations include estimating the EM loss between the first and second requested geographical points based on the virtual EM losses determined for the set of virtual loss lines.

A method and a network agent for providing cell assignment for a wireless device served by a network node. An input vector is created for a set of candidate cells based on measurements by the wireless device and/or by the network node related to performance and signals. A future effect of assigning the wireless device to a candidate cell is estimated for each candidate cell by applying the created input vector to an effect estimation function which may be a Q-learning function. A cell in the set of candidate cells is then determined and assigned for the wireless device, based on the estimated future effects of the candidate cells. The cell that provides the best future effect may be selected for cell assignment.

A method and a network agent for providing cell assignment for a wireless device served by a network node. An input vector is created for a set of candidate cells based on measurements by the wireless device and/or by the network node related to performance and signals. A future effect of assigning the wireless device to a candidate cell is estimated for each candidate cell by applying the created input vector to an effect estimation function which may be a Q-learning function. A cell in the set of candidate cells is then determined and assigned for the wireless device, based on the estimated future effects of the candidate cells. The cell that provides the best future effect may be selected for cell assignment.