Facilitating heterogeneous network analysis and resource planning for advanced networks

Abstract

Facilitating analysis and resource planning for advanced heterogeneous networks (e.g., 5G, 6G, and beyond) is provided herein. A system is provided that includes a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can include determining that a resource is to be added to existing resources at a grid level of a heterogeneous network. Further, the operations can include selecting candidate locations for placement of the resource based on a coverage-driven objective and a capacity-driven objective defined for the heterogeneous network. The coverage-driven objective can be associated with a demand for services within the grid level of the heterogeneous network. The capacity-driven objective can be associated with demand growth within the grid level of the heterogeneous network. The resource can be a fifth generation millimeter wave node or a cloud radio access network node.

What is claimed is: 
     
         1 . A method, comprising:
 based on a determination that a resource is to be added to existing resources at a grid level of a network, evaluating, by a system comprising a processor, a load weighted coverage score of the grid level of the network; and   based on the evaluating indicating that the load weighted coverage score of the grid level of the network satisfies a defined threshold, selecting, by the system, a candidate location for placement of the resource.   
     
     
         2 . The method of  claim 1 , wherein the selecting comprises:
 using a mixed integer programming model generated to be applicable to resource placement in the network.   
     
     
         3 . The method of  claim 2 , wherein the mixed integer programming model is configured to output a short term planning placement result and a long term planning placement result for the resource to be added to the existing resources at the grid level of the network. 
     
     
         4 . The method of  claim 1 , wherein the selecting comprises selecting the candidate location based on a coverage-driven objective and a capacity-driven objective defined for the network. 
     
     
         5 . The method of  claim 4 , wherein the coverage-driven objective is associated with a demand for services within the grid level of the network, and wherein the capacity-driven objective is associated with demand growth within the grid level of the network. 
     
     
         6 . The method of  claim 1 , further comprising:
 determining, by the system, a solution to a coverage-driven objective and a capacity-driven objective using a mixed integer programming model and based on a design configuration defined for the network.   
     
     
         7 . The method of  claim 6 , wherein using the mixed integer programming model comprises using a multi-step solution to determine the coverage-driven objective and the capacity-driven objective separately. 
     
     
         8 . The method of  claim 6 , wherein using the mixed integer programming model comprises using a multi-step solution to determine the coverage-driven objective and the capacity-driven objective concurrently. 
     
     
         9 . The method of  claim 1 , further comprising:
 determining, by the system, the load weighted coverage score comprising weighting the load weighted coverage score to restrict a quantity of fifth generation millimeter wave nodes in the grid level of the network to being at most a defined quantity.   
     
     
         10 . The method of  claim 1 , further comprising:
 determining, by the system, the load weighted coverage score comprising weighting the load weighted coverage score to restrict a quantity of cloud radio access network nodes located in the grid level of the network to being at most a defined quantity.   
     
     
         11 . A system, comprising:
 a processor; and   a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:
 based on an indication that a resource is to be placed within a defined grid area of a communication network, determining a load weighted coverage score of candidate locations for a placement of the resource within the defined grid area; and 
 based on the load weighted coverage score being determined to satisfy a defined score, selecting a placement location of the resource from a group of placement locations within the defined grid area, wherein the placement location is selected from the candidate locations. 
   
     
     
         12 . The system of  claim 11 , wherein the indication is a result of a demand for services within the defined grid area being determined to exceed a coverage capacity of the defined grid area, and wherein the selecting comprises selecting the placement location based on the resource, at the placement location, being determined to satisfy the demand for services. 
     
     
         13 . The system of  claim 11 , wherein the operations further comprise:
 implementing a mixed integer programming model built to output placements for resources in the communication network, and   wherein the mixed integer programming model outputs first placements applicable to short term planning for a defined short term time period and second placements applicable to long term planning for a defined long term time period, after the defined short term time period, for the defined grid area.   
     
     
         14 . The system of  claim 12 , wherein the operations further comprise:
 using a mixed integer programming model to select the placement location based on using a multi-step process to determine, separately, a coverage-driven objective and a capacity-driven objective for the communication network.   
     
     
         15 . The system of  claim 12 , wherein the operations further comprise:
 using a mixed integer programming model to select the placement location based on using a multi-step process to, at a same time or substantially the same time, determining a coverage-driven objective and a capacity-driven objective for the communication network.   
     
     
         16 . The system of  claim 11 , wherein the resource is a fifth generation millimeter wave node, and wherein the load weighted coverage score is selected to retain a quantity of fifth generation millimeter wave nodes in the defined grid area below a defined quantity. 
     
     
         17 . The system of  claim 11 , wherein the resource is a cloud radio access network node, and wherein the load weighted coverage score is selected to retain a quantity of cloud radio access network nodes located in the defined grid area below a defined quantity. 
     
     
         18 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
 based on a determination that demand for services exceeds a current capability of a communications network, determining candidate locations for a positioning of a resource within a grid area associated with the communications network; and   selecting the positioning of the resource in the candidate locations based on a load weighted coverage score of the grid area being determined to satisfy a specified threshold, wherein the load weighted coverage score is determined based on the positioning of the resource.   
     
     
         19 . The non-transitory machine-readable medium of  claim 18 , wherein the operations further comprise:
 facilitating the positioning of the resource within the grid area based on the candidate locations, wherein the positioning of the resource comprises offloading network traffic from an existing resource to the resource.   
     
     
         20 . The non-transitory machine-readable medium of  claim 18 , wherein the selecting comprises:
 using a mixed integer programming model applicable to solve resource placement in the communications network to select the positioning of the resource, wherein the mixed integer programming model considers short term planning and long term planning for the grid area, and wherein the short term planning represents a nearer amount of time to a present time than the long term planning.